cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7369809
title: Replication Package Literature Review AI and Mobile Energy

description: Replication Package for "Artificial Intelligence: Both the Problem and the Solution for Mobile Energy Consumption"
DOI: 10.5281/zenodo.6549585
title: Dataset for "The effect of parallel electron plateau on banded chorus generation: 1-D PIC simulations in mirror geometry"

description: Dataset for a paper "The effect of parallel electron plateau on banded chorus generation: 1-D PIC simulations in mirror geometry"
DOI: 10.5281/zenodo.6549584
title: Dataset for "The effect of parallel electron plateau on banded chorus generation: 1-D PIC simulations in mirror geometry"

description: Dataset for a paper "The effect of parallel electron plateau on banded chorus generation: 1-D PIC simulations in mirror geometry"
DOI: 10.5281/zenodo.6562438
title: Dataset for "The effect of parallel electron plateau on banded chorus generation: 1-D PIC simulations in mirror geometry"

description: Dataset for a paper "The effect of parallel electron plateau on banded chorus generation: 1-D PIC simulations in mirror geometry"
DOI: 10.5281/zenodo.7369841
title: tropidonophis_doriae_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.7369840
title: tropidonophis_doriae_zanfona_1.fasta.gz

description: .
DOI: 10.48324/dandi.000363/0.221127.2345
title: Mesoscale Activity Map Dataset

description: Mesoscale Activity Map Project. Map behavior-related activity in a multi-regional network supporting memory-guided movement in mice. Anatomy-guided recordings from multiple connected brain regions, from anterior lateral motor cortex to the medulla.

Supported by Simons Collaboration on the Global Brain, Janelia Visitor Project, NIH U19NS123714-01, R01NS112312, R01EB028171, McKnight foundation
DOI: 10.5281/zenodo.4404546
title: Pan-cancer analysis of mRNA stability for decoding tumour post-transcriptional programs

description: Supplemental data and analysis files for Perron et al.: "Pan-cancer analysis of mRNA stability for decoding tumour post-transcriptional programs" (https://www.nature.com/articles/s42003-022-03796-w). The .tar.gz files contain read counts associated with various RNA-seq analyses. The .rds files are single R object files that contain various analysis results tables. The .csv files also contain analysis results or sample metadata tables. See http://csg.lab.mcgill.ca/sup/pancancer_stability/ for a full description of the files.
description: {"references": ["Perron et al. (2022). Pan-cancer analysis of mRNA stability for decoding tumour post-transcriptional programs. Commun Biol 5:851"]}
DOI: 10.6084/m9.figshare.17898182
title: Routinely accessible parameters of mineralocorticoid receptor function, depression subtypes and response prediction: a post-hoc analysis from the early medication change trial in major depressive disorder

description: Previous studies indicated a relationship between aldosterone, the mineralocorticoid receptor (MR), and antidepressant treatment outcome. Physiological indicators of MR function (blood pressure and electrolytes) are easily accessible and may therefore serve as useful predictors. Thus, our aim was to investigate the predictive value of peripheral MR-related markers for antidepressant treatment outcomes. 826 MDD patients who had participated in the randomised-controlled Early Medication Change (EMC) trial were analysed. Depression severity and MR-related markers were assessed weekly. In 562 patients, genetic variation of five MR-related genes was determined. Patients with blood pressure &lt;120mmHg showed higher depression severity (<i>p</i> = 0.005) than patients with blood pressure ≥120mmHg. Patients with a melancholic subtype had significantly lower blood pressures (<i>p</i> = 0.004). Na<sup>+</sup>/K<sup>+</sup> ratio was positively and K<sup>+</sup>-concentration was negatively correlated to depression severity and to relative changes in HAMD from baseline to day 14, and 56 respectively (<i>p</i> &lt; 0.001). For none of the MR-related genes, genetic variation was associated with treatment outcomes. We confirmed early observations of an altered peripheral MR sensitivity, reflected by lower blood pressure, low K<sup>+</sup> or high Na<sup>+</sup>/K<sup>+</sup> ratio in patients with more severe depression. These routinely collected biomarkers may potentially be useful for risk stratification in an early stage of treatment. <b>Trial Registration:</b> clinicaltrials.gov Identifier: NCT00974155; https://www.clinicaltrials.gov/ct2/results?term=NCT00974155
DOI: 10.6084/m9.figshare.17898182.v1
title: Routinely accessible parameters of mineralocorticoid receptor function, depression subtypes and response prediction: a post-hoc analysis from the Early Medication Change trial in major depressive disorder

description: Previous studies indicated a relationship between aldosterone, the mineralocorticoid receptor (MR), and antidepressant treatment outcome. Physiological indicators of MR function (blood pressure and electrolytes) are easily accessible and may therefore serve as useful predictors. Thus, our aim was to investigate the predictive value of peripheral MR-related markers for antidepressant treatment outcome. 826 MDD patients who had participated in the randomized-controlled Early Medication Change (EMC) trial were analyzed. Depression severity and MR-related markers were assessed weekly. In 562 patients, genetic variation of five MR-related genes was determined. Patients with blood pressure &lt;120mmHg showed higher depression severity (p = 0.005) than patients with blood pressure ≥120mmHg. Patients with a melancholic subtype had significantly lower blood pressures (p = 0.004). Na<sup>+</sup>/K<sup>+</sup> ratio was positively and K<sup>+</sup>-concentration was negatively correlated to depression severity and to relative changes in HAMD from baseline to day14, and 56 respectively (p &lt; 0.001). For none of the MR-related genes, genetic variation was associated with treatment outcome. We confirmed early observations of an altered peripheral MR sensitivity, reflected by lower blood pressure, low K<sup>+</sup> or high Na<sup>+</sup>/K<sup>+</sup> ratio in patients with more severe depression. These routinely collected biomarkers may potentially be useful for risk stratification in an early stage of treatment.
DOI: 10.6084/m9.figshare.17898182.v2
title: Routinely accessible parameters of mineralocorticoid receptor function, depression subtypes and response prediction: a post-hoc analysis from the early medication change trial in major depressive disorder

description: Previous studies indicated a relationship between aldosterone, the mineralocorticoid receptor (MR), and antidepressant treatment outcome. Physiological indicators of MR function (blood pressure and electrolytes) are easily accessible and may therefore serve as useful predictors. Thus, our aim was to investigate the predictive value of peripheral MR-related markers for antidepressant treatment outcomes. 826 MDD patients who had participated in the randomised-controlled Early Medication Change (EMC) trial were analysed. Depression severity and MR-related markers were assessed weekly. In 562 patients, genetic variation of five MR-related genes was determined. Patients with blood pressure &lt;120mmHg showed higher depression severity (<i>p</i> = 0.005) than patients with blood pressure ≥120mmHg. Patients with a melancholic subtype had significantly lower blood pressures (<i>p</i> = 0.004). Na<sup>+</sup>/K<sup>+</sup> ratio was positively and K<sup>+</sup>-concentration was negatively correlated to depression severity and to relative changes in HAMD from baseline to day 14, and 56 respectively (<i>p</i> &lt; 0.001). For none of the MR-related genes, genetic variation was associated with treatment outcomes. We confirmed early observations of an altered peripheral MR sensitivity, reflected by lower blood pressure, low K<sup>+</sup> or high Na<sup>+</sup>/K<sup>+</sup> ratio in patients with more severe depression. These routinely collected biomarkers may potentially be useful for risk stratification in an early stage of treatment. <b>Trial Registration:</b> clinicaltrials.gov Identifier: NCT00974155; https://www.clinicaltrials.gov/ct2/results?term=NCT00974155
DOI: 10.5281/zenodo.7068887
title: The role of vegetation optimality in the Budyko framework

description: In this study, we test how vegetation optimality explains convergence on the Budyko-curve. This is done for five study sites along the North Australian Tropical Transect, five catchments in Australia and a selection of catchments of the CAMELS-dataset. We test the following hypotheses: - Model simulations based on vegetation optimality lead to a better reproduction of the empirical Budyko curve than model simulations without self-optimized vegetation.<br> - The empirical parameter <em>n</em> stays constant as climate changes, as long as vegetation cover and rooting depths stay constant.<br> - Changes in <em>n</em> values are a result of slowly varying, long-term vegetation properties. <strong>How to use this project</strong> This project contains all pre- and post-processing scripts for the model runs to test our hypotheses. All final figures are in the notebooks, as well as the supplementary analyses. These scripts can be re-used for other applications. This is also a Renku project. Hence, to use this repository for your own analysis, create a free login for renku, then fork the project and modify it to your liking. The simplest way to start this project is right from the Renku<br> platform - just click on the `Environments` tab and start a new session. This will start an interactive environment right in your browser. To work with the project anywhere outside the Renku platform,<br> click the `Settings` tab where you will find the<br> git repo URLs - use `git` to clone the project on whichever machine you want. Python and conda packages are in `requirements.txt` and `environment.yml`. Please consult the various documentation sources for renkulab(https://renkulab.io/).
description: Funded by the Luxembourg National Research Fund, ATTRACT programme (A16/SR/11254288)
DOI: 10.5281/zenodo.7068888
title: The role of vegetation optimality in the Budyko framework

description: In this study, we test how vegetation optimality explains convergence on the Budyko-curve. This is done for five study sites along the North Australian Tropical Transect, five catchments in Australia and a selection of catchments of the CAMELS-dataset. We test the following hypotheses: - Model simulations based on vegetation optimality lead to a better reproduction of the empirical Budyko curve than model simulations without self-optimized vegetation.<br> - The empirical parameter <em>n</em> stays constant as climate changes, as long as vegetation cover and rooting depths stay constant.<br> - Changes in <em>n</em> values are a result of slowly varying, long-term vegetation properties. <strong>How to use this project</strong> This project contains all pre- and post-processing scripts for the model runs to test our hypotheses. All final figures are in the notebooks, as well as the supplementary analyses. These scripts can be re-used for other applications. This is also a Renku project. Hence, to use this repository for your own analysis, create a free login for renku, then fork the project and modify it to your liking. The simplest way to start this project is right from the Renku<br> platform - just click on the `Environments` tab and start a new session. This will start an interactive environment right in your browser. To work with the project anywhere outside the Renku platform,<br> click the `Settings` tab where you will find the<br> git repo URLs - use `git` to clone the project on whichever machine you want. Python and conda packages are in `requirements.txt` and `environment.yml`. Please consult the various documentation sources for renkulab(https://renkulab.io/).
description: Funded by the Luxembourg National Research Fund, ATTRACT programme (A16/SR/11254288)
DOI: 10.5281/zenodo.7361655
title: The role of vegetation optimality in the Budyko framework

description: In this study, we test how vegetation optimality explains convergence on the Budyko-curve. This is done for five study sites along the North Australian Tropical Transect, five catchments in Australia and a selection of catchments of the CAMELS-dataset. We test the following hypotheses: - Model simulations based on vegetation optimality lead to a better reproduction of the empirical Budyko curve than model simulations without self-optimized vegetation.<br> - The empirical parameter <em>n</em> stays constant as climate changes, as long as vegetation cover and rooting depths stay constant.<br> - Changes in <em>n</em> values are a result of slowly varying, long-term vegetation properties. <strong>How to use this project</strong> This project contains all pre- and post-processing scripts for the model runs to test our hypotheses. All final figures are in the notebooks, as well as the supplementary analyses. These scripts can be re-used for other applications. This is also a Renku project. Hence, to use this repository for your own analysis, create a free login for renku, then fork the project and modify it to your liking. The simplest way to start this project is right from the Renku<br> platform - just click on the `Environments` tab and start a new session. This will start an interactive environment right in your browser. To work with the project anywhere outside the Renku platform,<br> click the `Settings` tab where you will find the<br> git repo URLs - use `git` to clone the project on whichever machine you want. Python and conda packages are in `requirements.txt` and `environment.yml`. Please consult the various documentation sources for renkulab(https://renkulab.io/).
description: Funded by the Luxembourg National Research Fund, ATTRACT programme (A16/SR/11254288)
DOI: 10.5281/zenodo.7369857
title: dataset

description: Dataset of information about 1000+ estates extracted from Tecnocasa for academic uses.
DOI: 10.5281/zenodo.7369856
title: dataset

description: Dataset of information about 1000+ estates extracted from Tecnocasa for academic uses.
DOI: 10.5281/zenodo.7369923
title: Teste (Métodos de Avaliação)

description: Teste (Métodos de Avaliação)
DOI: 10.5281/zenodo.7369922
title: Teste (Métodos de Avaliação)

description: Teste (Métodos de Avaliação)
DOI: 10.21227/sr2c-k812
title: Q&A Text in Chinese Online Medical Community

description: Using Python. we crawl a total of 18, 793 diabetes related Q&A between Jun. 1, 2016 and Sept. 1, 2020 on xywy.com, a famous Chinese Online Medical Community. Each data contains four parts of the question detail page: Title, Problem Description, User ID and Question Time, and three parts of the doctor’s answer page: Doctor ID, Answer Content and Answer Time. After preprocessing such as cleaning and deduplication, we finally obtain 18,521 valid data. Considering the Problem Description contains the background information of the doctor’s answer, we combine the two into the Answer Content, which is used as the text of the knowledge graph construction later.
DOI: 10.34740/kaggle/dsv/4603760
title: BBC News
DOI: 10.34740/kaggle/dsv/4603799
title: Polish-Ukrainian translations from Tatoeba
DOI: 10.34740/kaggle/dsv/4603952
title: OntoUML/UFO Catalog
DOI: 10.6084/m9.figshare.16677619.v2
title: The TCGA cancer data for the paper entitled "Feature Screening for Survival Trait with Application to TCGA High-dimensional Genomic Data"

description: The TCGA ESCA, PAAD, LUAD, and BRCA genomic data with survival traits for the paper entitled "Feature Screening for Survival Trait with Application to TCGA High-dimensional Genomic Data".
DOI: 10.6084/m9.figshare.16816654.v6
title: TCGA cancer genomic data and pathway information

description: TCGA cancer genomic data (ESCA , PAAD and LUAD) and pathway information (GO-BP, GO-CC, GO-MF, KEEG, Biocarter)
DOI: 10.6084/m9.figshare.16677619.v3
title: The TCGA cancer data for the paper entitled "Feature Screening for Survival Trait with Application to TCGA High-dimensional Genomic Data"

description: The TCGA ESCA, PAAD, LUAD, and BRCA genomic data with survival traits for the paper entitled "Feature Screening for Survival Trait with Application to TCGA High-dimensional Genomic Data".
DOI: 10.34740/kaggle/dsv/4604018
title: 💥📰 Daily Breaking News Twitter Dataset
DOI: 10.34740/kaggle/dsv/4604036
title: 🇺🇦 Ukraine Conflict Twitter Dataset
DOI: 10.6084/m9.figshare.20920219
title: MAP2B_GTDB_db_20220905

description: microbiome
DOI: 10.6084/m9.figshare.20920219.v5
title: MAP2B_GTDB_db_20220905

description: microbiome
DOI: 10.6084/m9.figshare.20920219.v4
title: MAP2B_GTDB_db_20220905

description: microbiome
DOI: 10.14457/rmutk.the.2022.8
title: The impact of hollywood movies on chinese consumers' consumption behavior towards movies

description: Hollywood films have significantly influenced and appealed to the Chinese film market. The great success of Hollywood films introduced in China objectively reflects that Hollywood films conform to the consumption preferences of Chinese film audiences to a certain extent. As a starting point, this paper selects the Chinese mainstream moviegoers as the research object and, based on the theory of rational behaviour (TRA) and combined with the existing research results, summarizes the main factors affecting the attitudes and subjective norms of Chinese movie audiences towards overseas films, and establishes a research hypothesis model. Conclude using SPSS26.0 analysis software to verify the hypothesis between attitudes, subjective norms and movie-watching behavioural intentions. Thestudy found that Hollywood films' film characteristics and marketing strategies will positively affect the audience's perspective and the second audience's self-satisfied needs. Marketing strategy
description: the attitude and subjective norms of the third audience will positively impact the behaviour intention of watching movies, and the influence of attitude on behaviour intention is more significant than that of subjective norms. Based on the empirical results,policy optimization suggestions are put forward for the Chinese film industry.
DOI: 10.21947/1328871
title: Surface Meteorological Station - ARL 2m, ancillary flux, Boardman - Raw Data

description: These data contain measurements from a four-component net radiometer, as well as 2-m temperature, pressure, and relative humidity. In addition, soil moisture and temperature measurements and soil heat fluxes are included.
DOI: 10.21947/1328872
title: Lidar - DOE ARM StreamLine Doppler Lidar (Halo) - Raw Data

description: Refer to attached documentation.
DOI: 10.21947/1328882
title: Surface Meteorological Station - ARL 2m, Sonic, Wasco - Raw Data

description: The dataset provides wind, temperature, and turbulence measurements.
DOI: 10.21947/1328887
title: Surface Meteorological Station - ARL 2m, Sonic + irga flux, Boardman - Raw Data

description: These data are for calculating eddy covariance flux measurements.
DOI: 10.21947/1328898
title: Surface Meteorological Station - ARL 2m, ancillary flux, Prineville - Raw Data

description: These data contain measurements from a 4-component net radiometer, as well as 2-m temperature, pressure, and relative humidity (RH). Measurements of soil moisture and temperature and soil heat fluxes also are included in the dataset.
DOI: 10.21947/1328922
title: Surface Meteorological Station - ARL 10m, Boardman - Raw Data

description: The dataset provides 1-minute measurements of basic meteorological variables.
DOI: 10.21947/1349271
title: Aircraft Flight Data - Long-EZ aircraft - Raw Data

description: IMPOWR_EZF_QC_20Hz_YYYYMMDD.txt These files contain the observational data from the EZFlight aircraft that was taken and has been processed by the Stonybrook team. All data here is 20 Hz.
DOI: 10.21947/1349272
title: Tower - Cape Wind (CW) meteorological tower Nantucket Sound - Raw Data

description: Raw data from Cape Wind (CW) meteorological tower Nantucket Sound tower
DOI: 10.21947/1401988
title: Surface Meteorological Station - UND 21m, (3) Sonics 3m 10m 21m, (2) T/RH 3m 21m, Physics site-6 - Raw Data

description: The data included features wind, temperature, and turbulence measurements.
DOI: 10.21947/1401995
title: Surface Meteorological Station - ESRL Short Tower, Condon - Raw Data

description: A diversity of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.
DOI: 10.21947/1402003
title: Surface Meteorological Station - UND 10m, (2) Sonics 3m 10m, (2) T/RH 3m 10m (1) Licor 3m, Physics site-1 - Raw Data

description: The data included features wind, temperature, and turbulence measurements.
DOI: 10.21947/1402021
title: Surface Meteorological Station - ESRL Short Tower, Prineville - Raw Data

description: A diversity of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.
DOI: 10.21947/1402025
title: Surface Meteorological Station - ESRL Short Tower, Bonneville - Raw Data

description: A diversity of instruments are used to measure various quantities related to meteorology and precipitation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.
DOI: 10.21947/1428195
title: Radar - 915MHz - Ainsworth, NE - Raw Data

description: Raw data captured from the NOAA ESRL website: https://www.esrl.noaa.gov/psd/data/obs/data/ Hourly Radar Winds (WwWind) and Temperature (WwTemp)
DOI: 10.21947/1428196
title: Radar - 915MHz - De Smet, SD - Raw Data

description: Raw data captured from the NOAA ESRL website: https://www.esrl.noaa.gov/psd/data/obs/data/ Hourly Radar Winds (WwWind) and Temperature (WwTemp)
DOI: 10.21947/1428197
title: Radar - 915MHz - Leeds, ND - Raw Data

description: Raw data captured from the NOAA ESRL website: https://www.esrl.noaa.gov/psd/data/obs/data/ Hourly Radar Winds (WwWind) and Temperature (WwTemp)
DOI: 10.21947/1428198
title: Radar - 449MHz - Buffalo, SD - Raw Data

description: Raw data captured from the NOAA ESRL website: https://www.esrl.noaa.gov/psd/data/obs/data/ Hourly Radar Winds (WwWind) and Temperature (WwTemp)
DOI: 10.21947/1428199
title: Radar - 915MHz - Saint James, MN - Raw Data

description: Raw data captured from the NOAA ESRL website: https://www.esrl.noaa.gov/psd/data/obs/data/ Hourly Radar Winds (WwWind) and Temperature (WwTemp)
DOI: 10.21947/1428200
title: Radar - 915MHz - Mobridge, SD - Raw Data

description: Raw data captured from the NOAA ESRL website: https://www.esrl.noaa.gov/psd/data/obs/data/ Hourly Radar Winds (WwWind) and Temperature (WwTemp)
DOI: 10.21947/1428202
title: Radar - 449MHz - Valley City, ND - Raw Data

description: Raw data captured from the NOAA ESRL website: https://www.esrl.noaa.gov/psd/data/obs/data/ Hourly Radar Winds (WwWind) and Temperature (WwTemp)
DOI: 10.21947/1428203
title: Radar - 915MHz - Souix City, IA - Raw Data

description: Raw data captured from the NOAA ESRL website: https://www.esrl.noaa.gov/psd/data/obs/data/ Hourly Radar Winds (WwWind) and Temperature (WwTemp)
DOI: 10.21947/1429038
title: Sodar - Ozona, TX - Raw Data

description: Raw data from Ozona, TX sodar
DOI: 10.21947/1429039
title: Sodar - Brady, TX - Raw Data

description: Raw data from Brady, TX sodar
DOI: 10.21947/1429040
title: Sodar - Colorado City, TX - Raw Data

description: Raw data from Colorado City, TX sodar
DOI: 10.21947/1429041
title: Sodar - Reagan, TX - Raw Data

description: Raw data Reagan, TX sodar