cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10057926
title: 30-m Spatial Resolution Bioclimatic Dataset of 1991-2020 Climate Normals for Hubei Province, the Yangtze River Middle Reaches

description: Brief Introduction of the Dataset

This bioclimatic dataset is the product of research article "Mapping 30-m Resolution Bioclimatic Variables During 1991-2020 Climate Normals for Hubei Province, the Yangtze River Middle Reaches." published in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

The dataset contains 19 30-m resolution average bioclimatic variables during 1991-2020 Climate Normals for Hubei Province (108°21′42″—116°07′50″ E, 29°01′53″—33°6′47″ N), the core region of the Yangtze River middle reaches. The dataset was constructed by statistically downscaling the Climatic Research Unit (CRU) 1-km monthly climate variables (1440 in total), cablirating with ground observation data with 82 weather stations and aggregating based on the defination of 19 bioclimatic variables. The downscaling of four 1-km Climatic Research Unit monthly climate variables including monthly maximum, mean, minimum temperature and precipitation was firstly achieved by random forest model with 30-m resolution terrain and spatial data. Then the interpolation-based geographical differential analysis (GDA) was applied to improve the accuracy of downscaled products based on ground observation data. Finally, the bioclimatic variables were aggregated based on their definitions and averaged for the 30 years. The Yangtze River middle reaches is abundant of forestry, agriculture, biodiversity resources that requires finer bioclimatic data for better understands of these aspects. This dataset will provide higher spatial accuracy, more information and applicability in finer regional studies in the Yangtze River middle reaches.

 

Description of the 19 Bioclimatic Variables

The dataset contains 19 geotiff files in total. File names and the corresponding full name of bioclimatic variables are described as follows:

Bio01 Mean annual air temperature (℃)Bio02 Mean diurnal air temperature range (℃)Bio03 Isothermality (%)Bio04 Temperature seasonality (℃)Bio05 Mean daily maximum air temperature of the warmest month (℃)Bio06 Mean daily minimum air temperature of the coldest month (℃)Bio07 Annual range of air temperature (℃)Bio08 Mean daily mean air temperatures of the wettest quarter (℃)Bio09 Mean daily mean air temperatures of the driest quarter (℃)Bio10 Mean daily mean air temperatures of the warmest quarter (℃)Bio11 Mean daily mean air temperatures of the coldest quarter (℃)Bio12 Annual precipitation amount (mm)Bio13 Precipitation amount of the wettest month (mm)Bio14 Precipitation amount of the driest month (mm)Bio15 Precipitation seasonality (%)Bio16 Precipitation amount of the wettest quarter (mm)Bio17 Precipitation amount of the driest quarter (mm)Bio18 Precipitation amount of the warmest quarter (mm)Bio19 Precipitation amount of the coldest quarter (mm)

 

Others

More information related to bioclimatic variables can be found on https://chelsa-climate.org/bioclim/
DOI: 10.5281/zenodo.8266892
title: 30-m Spatial Resolution Bioclimatic Dataset of 1991-2020 Climate Normals for Hubei Province, the Yangtze River Middle Reaches

description: Brief Introduction of the Dataset

This bioclimatic dataset is the product of research article "Mapping 30-m Resolution Bioclimatic Variables During 1991-2020 Climate Normals for Hubei Province, the Yangtze River Middle Reaches." published in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

The dataset contains 19 30-m resolution average bioclimatic variables during 1991-2020 Climate Normals for Hubei Province (108°21′42″—116°07′50″ E, 29°01′53″—33°6′47″ N), the core region of the Yangtze River middle reaches. The dataset was constructed by statistically downscaling the Climatic Research Unit (CRU) 1-km monthly climate variables (1440 in total), cablirating with ground observation data with 82 weather stations and aggregating based on the defination of 19 bioclimatic variables. The downscaling of four 1-km Climatic Research Unit monthly climate variables including monthly maximum, mean, minimum temperature and precipitation was firstly achieved by random forest model with 30-m resolution terrain and spatial data. Then the interpolation-based geographical differential analysis (GDA) was applied to improve the accuracy of downscaled products based on ground observation data. Finally, the bioclimatic variables were aggregated based on their definitions and averaged for the 30 years. The Yangtze River middle reaches is abundant of forestry, agriculture, biodiversity resources that requires finer bioclimatic data for better understands of these aspects. This dataset will provide higher spatial accuracy, more information and applicability in finer regional studies in the Yangtze River middle reaches.

 

Description of the 19 Bioclimatic Variables

The dataset contains 19 geotiff files in total. File names and the corresponding full name of bioclimatic variables are described as follows:

Bio01 Mean annual air temperature (℃)Bio02 Mean diurnal air temperature range (℃)Bio03 Isothermality (%)Bio04 Temperature seasonality (℃)Bio05 Mean daily maximum air temperature of the warmest month (℃)Bio06 Mean daily minimum air temperature of the coldest month (℃)Bio07 Annual range of air temperature (℃)Bio08 Mean daily mean air temperatures of the wettest quarter (℃)Bio09 Mean daily mean air temperatures of the driest quarter (℃)Bio10 Mean daily mean air temperatures of the warmest quarter (℃)Bio11 Mean daily mean air temperatures of the coldest quarter (℃)Bio12 Annual precipitation amount (mm)Bio13 Precipitation amount of the wettest month (mm)Bio14 Precipitation amount of the driest month (mm)Bio15 Precipitation seasonality (%)Bio16 Precipitation amount of the wettest quarter (mm)Bio17 Precipitation amount of the driest quarter (mm)Bio18 Precipitation amount of the warmest quarter (mm)Bio19 Precipitation amount of the coldest quarter (mm)

 

Others

More information related to bioclimatic variables can be found on https://chelsa-climate.org/bioclim/
DOI: 10.34740/kaggle/dsv/7493191
title: MoviesDatasets
DOI: 10.34740/kaggle/dsv/7493196
title: Mahanagar Yatayat Transport Dataset
DOI: 10.6084/m9.figshare.24559240
title: <b>Table S1. Strain, ecological information, MUCL deposition identifiers, genomic data overview and sources of publication for pre-existing genomes.</b>

description: Mycena comparative genomics.
DOI: 10.6084/m9.figshare.24559240.v1
title: <b>Table S1. Strain, ecological information, MUCL deposition identifiers, genomic data overview and sources of publication for pre-existing genomes.</b>

description: Mycena comparative genomics.
DOI: 10.6084/m9.figshare.24559240.v2
title: <b>Table S1. Strain, ecological information, MUCL deposition identifiers, genomic data overview and sources of publication for pre-existing genomes.</b>

description: Mycena comparative genomics.
DOI: 10.6084/m9.figshare.25093502
title: Effects of Long-COVID-19 infection during pregnancy on adverse pregnancy outcomes: a retrospective cohort study in China

description: This is the original data set of Long-COVID-19 during pregnancy in the First People's Hospital of Kunming, Yunnan, China. A retrospective cohort study was conducted to select 660 pregnant women who were hospitalized for delivery in our hospital from January 1, 2022 to July 31, 2023.
DOI: 10.6084/m9.figshare.25093502.v1
title: Effects of Long-COVID-19 infection during pregnancy on adverse pregnancy outcomes: a retrospective cohort study in China

description: This is the original data set of Long-COVID-19 during pregnancy in the First People's Hospital of Kunming, Yunnan, China. A retrospective cohort study was conducted to select 660 pregnant women who were hospitalized for delivery in our hospital from January 1, 2022 to July 31, 2023.
DOI: 10.5281/zenodo.10564834
title: Data for Electron-induced non-monotonic pressure dependence of the lattice thermal conductivity of θ-TaN.

description: Data for Electron-induced non-monotonic pressure dependence of the lattice thermal conductivity of θ-TaN

Contents:

Structure file, harmonic (second-order) and anharmonic (third- and fourth-order) interatomic force constants (IFCs) and phonon-electron scattering rates, and the input files for the FourPhonon package for each pressure considered in the paper.
DOI: 10.48579/pro/gzq9fn
title: Série temporelles des concentrations de Hg, Cd et Pb chez le dauphin et marsouin commun

description: Il s'agit des concentrations de trois éléments chimiques non-essentiels, le mercure (Hg) le cadmium (Cd) et le plomb (Pb), analysés dans les tissues de stockage (i.e. foie et rein) de deux espèces de cétacés qui sont présents dans les eaux françaises Atlantiques y compris la Manche, le dauphin commun (Delphinus delphis) et le marsouin commun (Phocoena phocoena). Ces données de concentrations correspondent à un jeu de données de 18 ans acquis au travers de différents projet de recherche mais surtout au travers du suivi de la contamination chimique chez les cétacés réalisé par l'observatoire Pelagis depuis 2017. Les concentrations de contaminants comme le Hg, le Cd et le Pb sont fortement affecté par des facteurs biologiques (par exemple l'âge, le sexe) et écologiques (par exemple l'habitat, régime alimentaire) dépendant de l'espèce et même de l'individu. Pour cette raison, l'analyse des tendances temporelles de ces trois éléments chimiques pour chacune des deux espèces a été réalisé en utilisant des données d'âge, le sexe et la zone où l'animal a été retrouvé échoué. Quand les données d'âge ne sont pas disponibles nous utilisons la taille des animaux comme indicateur de l'âge. Également, comme proxy de la zone d'alimentation et des habitudes alimentaires nous avons utilisé les ratios isotopiques de carbone et azote dans les modèles de tendances temporelles. Pour finir, nous avons analysé le ratio molaire selenium:mercure (Se:Hg) comme indicateur de la capacité de détoxification de Hg grâce au selenium biodisponible des individus étudiés.
DOI: 10.12770/e52fd18b-745c-4b63-8234-af151daed0ee
title: Données des campagnes halieutiques : Surveillance des contaminants chimiques dans les poissons - CoRePh (2017 à 2018)

description: Mesures de concentrations en contaminants chimiques (organiques et métalliques) dans des poissons et céphalopodes prélevés lors des campagnes halieutiques mises en œuvre par l'Ifremer : EVHOE 2017, CGFS 2018, MEDITS 2017 et PELMED 2017.
DOI: 10.17882/59178
title: Cruise 64PE431: a south to north hydrographic transect in the central Caribbean Sea in February 2018.

description: This data set consist of CTD data collected on board the RV Pelagia during cruise 64PE431. This cruise started in Oranjestad, Aruba on 4 February 2018 and ended in Phillipsburg, Sint Maarten on 11 February 2018. The cruise consisted of a survey across the Caribbean Sea, with hydrographic stations performed between the 1200 m isobath north of Aruba to the 800 m isobaths just south of the Dominican Republic. The aim of these measurements was to document the background hydrography of the Caribbean Sea as well as the hydrographic and velocity structure of a Caribbean anticyclone. A large anticyclone was detected north of Aruba with the use of altimetry field before the start of the cruise. The CTD station positions were adjusted in order to transect the center of this anticyclone. Furthermore four Argo floats provided by the European Argo program were deployed at hydrographic stations. Details on the floatnumbers can be found in the cruise report.
DOI: 10.17882/75639
title: Reconstructing Global Chlorophyll-a Variations Using a Non-linear Statistical Approach

description:  Monitoring the spatio-temporal variations of surface chlorophyll-a concentration (Chl, a proxy of phytoplankton biomass) greatly benefited from the availability of continuous and global ocean color satellite measurements from 1997 onward. These two decades of satellite observations are however still too short to provide a comprehensive description of Chl variations at decadal to multi-decadal timescales. This paper investigates the ability of a machine learning approach (a non-linear statistical approach based on Support Vector Regression, hereafter SVR) to reconstruct global spatio-temporal Chl variations from selected surface oceanic and atmospheric physical parameters. With a limited training period (13 years), we first demonstrate that Chl variability from a 32-years global physical-biogeochemical simulation can generally be skillfully reproduced with a SVR using the model surface variables as input parameters. We then apply the SVR to reconstruct satellite Chl observations using the physical predictors from the above numerical model and show that the Chl reconstructed by this SVR more accurately reproduces some aspects of observed Chl variability and trends compared to the model simulation. This SVR is able to reproduce the main modes of interannual Chl variations depicted by satellite observations in most regions, including El Niño signature in the tropical Pacific and Indian Oceans. In stark contrast with the trends simulated by the biogeochemical model, it also accurately captures spatial patterns of Chl trends estimated by satellite data, with a Chl increase in most extratropical regions and a Chl decrease in the center of the subtropical gyres, although the amplitude of these trends are underestimated by half. Results from our SVR reconstruction over the entire period (1979–2010) also suggest that the Interdecadal Pacific Oscillation drives a significant part of decadal Chl variations in both the tropical Pacific and Indian Oceans. Overall, this study demonstrates that non-linear statistical reconstructions can be complementary tools to in situ  and satellite observations as well as conventional physical-biogeochemical numerical simulations to reconstruct and investigate Chl decadal variability.
DOI: 10.17882/97201
title: Modelling microplastic bioaccumulation and biomagnification potential in the Galápagos penguin ecosystem using Ecopath and Ecosim (EwE) with Ecotracer

description: This dataset, generated through trophodynamic Ecopath with Ecosim (EwE) modeling and Ecotracer, encompasses two models—a novel model by McMullen K. (2023), featuring the Galápagos penguin as a sentinel species for microplastics in the Galápagos Marine Reserve, and a model by Ruiz DJ and Wolff M (2011) focusing on the Bolivar Channel ecosystem. Empirical data was collected in October 2021 (McMullen K., 2023), including surface seawater, zooplankton, penguin prey, and penguin scat. This empirical data and other data from available literature feed the model. Various scenarios, notably a 99% elimination rate, were employed to gauge model sensitivity. Results indicate that microplastics can accumulate in predator-prey relationships, with biomagnification contingent on elimination rates. This underscores the urgent need for further investigation into the elimination rates of distinct plastics, addressing a crucial gap in contemporary microplastic ecotoxicology and bioaccumulation research. The dataset offers valuable insights into the complex dynamics of plastic pollution in marine ecosystems, urging heightened scrutiny of plastic elimination rates for informed conservation strategies.
For methods see McMullen K. (2023).
References: 
McMullen K. The Galápagos penguin as the “canary in the coal mine” for microplastics research in the Galápagos Marine Reserve & plastic pollution perceptions in Ecuadorian mangrove communities. M.Sc. Thesis. University of British Columbia. 2023. Available from: http://hdl.handle.net/2429/84664
Ruiz DJ, Wolff M. The Bolivar Channel Ecosystem of the Galapagos Marine Reserve: Energy flow structure and role of keystone groups. J. Sea. Res. 2011 Aug;66(2):123–34.
DOI: 10.6084/m9.figshare.24512290
title: <b>Repeated cannabidiol treatment affects neuroplasticity and endocannabinoid signaling in the prefrontal cortex of the Flinders Sensitive Line (FSL) rat model of depression</b>

description: Delayed therapeutic responses and limited efficacy are the main challenges of existing antidepressant drugs, thereby incentivizing the search for new potential treatments. Cannabidiol (CBD), non-psychotomimetic component of cannabis, has shown promising antidepressant effects in different rodent models, but its mechanism of action remains unclear. Herein, we investigated the antidepressant-like effects of repeated CBD treatment on behavior, neuroplasticity markers and lipidomic profile in the prefrontal cortex (PFC) of Flinders Sensitive Line (FSL), a genetic animal model of depression, and their control counterparts Flinders Resistant Line (FRL) rats. Male FSL animals were treated with CBD (10 mg/kg; i.p.) or vehicle (7 days) followed by Open Field Test (OFT) and the Forced Swimming Test (FST). The PFC was analyzed by a) western blotting to assess markers of synaptic plasticity and cannabinoid signaling in synaptosome and cytosolic fractions; b) mass spectrometry-based lipidomics to investigate endocannabinoid levels (eCB). CBD attenuated the increased immobility observed in FSL, compared to FRL in FST, without changing the locomotor behavior in the OFT. In synaptosomes, CBD increased ERK1, mGluR5, and Synaptophysin, but failed to reverse the reduced CB1 and CB2 levels in FSL rats. In the cytosolic fraction, CBD increased ERK2 and decreased mGluR5 expression in FSL rats. Surprisingly, there were no significant changes in eCB levels in response to CBD treatment. These findings suggest that CBD effects in FSL animals are associated with changes in synaptic plasticity markers involving mGluR5, ERK1, ERK2, and synaptophysin signaling in the PFC, without increasing the levels of endocannabinoids in this brain region.
DOI: 10.6084/m9.figshare.24512290.v1
title: <b>Repeated cannabidiol treatment affects neuroplasticity and endocannabinoid signaling in the prefrontal cortex of a rat model of depression</b>

description: Delayed therapeutic responses and limited efficacy are the main challenges of existing antidepressant drugs, thereby incentivizing the search for new potential treatments. Cannabidiol (CBD), a non-psychostimulant component of cannabis, has shown promising antidepressant effects in different rodent models, but its mechanism of action remains unclear. Herein, we investigated the antidepressant-like effects of repeated CBD treatment on behavior, neuroplasticity markers and lipidomic (endocannabinoid) profile in the prefrontal cortex (PFC) of Flinders Sensitive Line (FSL), an animal model of depression, along with their control counterparts, the Flinders Resistant Line (FRL) rats. Male FSL animals were treated with CBD (10 mg/kg; i.p.) or vehicle (7 days) followed by Open Field Test (OFT) and the Forced Swimming Test (FST). The PFC was analyzed by a) western blotting to assess markers of synaptic plasticity and cannabinoid signaling in synaptosome and cytosolic fractions (CB1, CB2, CaMKII, Akt, eEF2, GSK-3β, ERK1, ERK2, mGluR5, PSD95, Synaptophysin, BDNF, TrkB); b) mass spectrometry-based lipidomics to investigate endocannabinoid levels (eCB). CBD attenuated the increased immobility observed in FSL, compared to FRL in FST, without changing the locomotor behavior in the OFT. In synaptosomes, CBD increased ERK1, mGluR5, and Synaptophysin, but failed to reverse the reduced CB1 and CB2 levels in FSL rats. In the cytosolic fraction, CBD increased ERK2 and decreased mGluR5 expression in FSL rats. Surprisingly, there were no significant changes in eCB levels in response to CBD treatment. These findings suggest that CBD effects in FSL animals are associated with changes in synaptic plasticity markers involving mGluR5, ERK1, ERK2, and synaptophysin signaling in the PFC, without increasing the levels of endocannabinoids in this brain region.
DOI: 10.6084/m9.figshare.25037678
title: research_data.xlsx

description: This is the dataset of assets and liabilities of 145 U.S. SLHCs (2013-2023).
DOI: 10.5281/zenodo.10565556
title: White Noise Files

description: White Noise Files contain the comma delimited time series data file as well as all the raw stationarity test result files.
DOI: 10.5281/zenodo.10565562
title: Nonstationary White Noise Files

description: Nonstationary White Noise Files contain the comma delimited time series data file as well as all the raw stationarity test result files.
DOI: 10.5281/zenodo.10565550
title: Sine Files

description: Sine Files contain the comma delimited time series data file as well as all the raw stationarity test result files.
DOI: 10.5281/zenodo.10565555
title: White Noise Files

description: White Noise Files contain the comma delimited time series data file as well as all the raw stationarity test result files.
DOI: 10.5281/zenodo.10565561
title: Nonstationary White Noise Files

description: Nonstationary White Noise Files contain the comma delimited time series data file as well as all the raw stationarity test result files.
DOI: 10.5281/zenodo.10565549
title: Sine Files

description: Sine Files contain the comma delimited time series data file as well as all the raw stationarity test result files.
DOI: 10.5281/zenodo.10565545
title: Autoregressive Order 1 Unit Root Time Varying Files

description: Autoregressive Order 1 Unit Root Time Varying Files contain the comma delimited time series data file as well as all the raw stationarity test result files.
DOI: 10.5281/zenodo.10565546
title: Autoregressive Order 1 Unit Root Time Varying Files

description: Autoregressive Order 1 Unit Root Time Varying Files contain the comma delimited time series data file as well as all the raw stationarity test result files.
DOI: 10.5281/zenodo.10565519
title: Autoregressive Order 1 Moving Average Order 1 Unit Root Less Than One Files

description: Autoregressive Order 1 Moving Average Order 1 Unit Root Less Than One Files contain the comma delimited time series data file as well as all the raw stationarity test result files.
DOI: 10.5281/zenodo.10575699
title: ANALYZING THE INFLUENCE OF DIFFERENT ASPECTS OF PHYSICAL FITNESS ON COGNITIVE AND ACADEMIC ACHIEVEMENT IN YOUTH
DOI: 10.5281/zenodo.10575700
title: ANALYZING THE INFLUENCE OF DIFFERENT ASPECTS OF PHYSICAL FITNESS ON COGNITIVE AND ACADEMIC ACHIEVEMENT IN YOUTH
DOI: 10.5281/zenodo.10565520
title: Autoregressive Order 1 Moving Average Order 1 Unit Root Less Than One Files

description: Autoregressive Order 1 Moving Average Order 1 Unit Root Less Than One Files contain the comma delimited time series data file as well as all the raw stationarity test result files.
DOI: 10.5281/zenodo.10565533
title: Autoregressive Order 1 Unit Root Greater Than One Files

description: Autoregressive Order 1 Unit Root Greater Than One Files contain the comma delimited time series data file as well as all the raw stationarity test result files.
DOI: 10.5281/zenodo.10565534
title: Autoregressive Order 1 Unit Root Greater Than One Files

description: Autoregressive Order 1 Unit Root Greater Than One Files contain the comma delimited time series data file as well as all the raw stationarity test result files.
DOI: 10.5281/zenodo.10565510
title: Autoregressive Order 1 Unit Root Less Than One Files

description: Autoregressive Order 1 Unit Root Less Than One Files contain the comma delimited time series data file as well as all the raw stationarity test result files.
DOI: 10.5281/zenodo.10565511
title: Autoregressive Order 1 Unit Root Less Than One Files

description: Autoregressive Order 1 Unit Root Less Than One Files contain the comma delimited time series data file as well as all the raw stationarity test result files.
DOI: 10.5281/zenodo.10565502
title: Autoregressive Order 2 Unit Root Less Than or Equal One Files

description: Autoregressive Order 2 Unit Root Less Than or Equal One Files contain the comma delimited time series data file as well as all the raw stationarity test result files.
DOI: 10.5281/zenodo.10565501
title: Autoregressive Order 2 Unit Root Less Than or Equal One Files

description: Autoregressive Order 2 Unit Root Less Than or Equal One Files contain the comma delimited time series data file as well as all the raw stationarity test result files.