cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4037442
title: Final results from McCoy et al., 'Global Observations of Submesoscale Coherent Vortices in the Ocean', submitted to Progress in Oceanography.

description: Submesoscale coherent vortices (SCVs) are small-scale, subsurface eddies that are ubiquitous in the ocean. Observations suggest that they efficiently trap and transport water, nutrients, and other properties thousands of kilometers away from their formation regions. However, the weak sea-surface signature restricts SCV observations to mostly chance encounters with shipboard subsurface instrumentation. Thus, the global occurrence, properties, and generation frequency of SCVs remain poorly constrained. Here we present results from a new algorithm used to identify SCVs from Argo float data, applied to roughly 2 million profiles conducted globally from August 1997 to January 2020. After application of the SCV detection algorithm to the global Argo array, we identify 2501 casts piercing spicy-core SCVs (those with anomalously hot and salty water mass characteristics), and 1583 casts piercing minty-core SCVs (anomalously cold and fresh cores) over more than 20 years of available data. The Matlab file 'final_individual_scvs.mat' contains various data for each SCV identified. By grouping detections from consecutive Argo casts, we are also able to record 383 spicy-core SCV time-series and 169 minty-core SCV time-series. The Matlab file 'final_timeseries_scvs.mat' contains the data for these time-series. For a more detailed description of each Matlab file, please see 'README.rtf'. Reach out to Daniel McCoy (dmccoy801@gmail.com) or Daniele Bianchi (dbianchi@atmos.ucla.edu) for inquiries.
description: For access to codes used to generate the final results, please go to: https://doi.org/10.5281/zenodo.4037179
DOI: 10.5281/zenodo.4037441
title: Final results from McCoy et al., 'Global Observations of Submesoscale Coherent Vortices in the Ocean', submitted to Progress in Oceanography.

description: Submesoscale coherent vortices (SCVs) are small-scale, subsurface eddies that are ubiquitous in the ocean. Observations suggest that they efficiently trap and transport water, nutrients, and other properties thousands of kilometers away from their formation regions. However, the weak sea-surface signature restricts SCV observations to mostly chance encounters with shipboard subsurface instrumentation. Thus, the global occurrence, properties, and generation frequency of SCVs remain poorly constrained. Here we present results from a new algorithm used to identify SCVs from Argo float data, applied to roughly 2 million profiles conducted globally from August 1997 to January 2020. After application of the SCV detection algorithm to the global Argo array, we identify 2501 casts piercing spicy-core SCVs (those with anomalously hot and salty water mass characteristics), and 1583 casts piercing minty-core SCVs (anomalously cold and fresh cores) over more than 20 years of available data. The Matlab file 'final_individual_scvs.mat' contains various data for each SCV identified. By grouping detections from consecutive Argo casts, we are also able to record 383 spicy-core SCV time-series and 169 minty-core SCV time-series. The Matlab file 'final_timeseries_scvs.mat' contains the data for these time-series. For a more detailed description of each Matlab file, please see 'README.rtf'. Reach out to Daniel McCoy (dmccoy801@gmail.com) or Daniele Bianchi (dbianchi@atmos.ucla.edu) for inquiries.
description: For access to codes used to generate the final results, please go to: https://doi.org/10.5281/zenodo.4037179
DOI: 10.6084/m9.figshare.12253043
title: Salish Sucker seasonal hypoxia data 2020 B.csv

description: This is an Excel spreadsheet that includes the data that was used in the analysis described in "Effects of landscape-scale hypoxia on Salish sucker and salmonid habitat associations", in review in the Canadian Journal of Fisheries and Aquatic Sciences. <b><br><br></b>
DOI: 10.6084/m9.figshare.12253043.v1
title: Salish Sucker seasonal hypoxia data 2020 B.csv

description: This is an Excel spreadsheet that includes the data that was used in the analysis described in "Effects of landscape-scale hypoxia on Salish sucker and salmonid habitat associations", in review in the Canadian Journal of Fisheries and Aquatic Sciences. <b><br><br></b>
DOI: 10.21227/pdsr-tf58
title: Gaussian Blobs of Varying numbers of samples, centers and features

description: The dataset has Gaussian Blobs of varying samples, centers and features. The datasets can be used for testing clustering algorithms for their effectiveness. This dataset is related to a paper which is under review.
DOI: 10.21227/yn0j-cx13
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains IDs and sentiment scores of the geo-tagged tweets related to the COVID-19 pandemic. The tweets are captured by an on-going project deployed at https://live.rlamsal.com.np. The model monitors the real-time Twitter feed for coronavirus-related tweets using 90+ different keywords and hashtags that are commonly used while referencing the pandemic. Complying with Twitter's content redistribution policy, only the tweet IDs are shared. You can re-construct the dataset by hydrating these IDs. The tweet IDs in this dataset belong to the tweets tweeted providing an exact location.-------------------------------------Recently added: Coronavirus (COVID-19) Tweets Sentiment Trend (Global)-------------------------------------Below is the quick overview of this dataset.— Dataset name: GeoCOV19Tweets Dataset— Number of tweets : 238,272 tweets— Coverage : Global— Language : English (EN)— Primary dataset : Coronavirus (COVID-19) Tweets Dataset (COV19Tweets Dataset)— Dataset updates : Everyday— Usage policy : As per Twitter's Developer Policy— Active keywords and hashtags: keywords.tsvPlease visit this page (primary dataset) for details regarding the collection date and time of each CSV file present in this dataset.
DOI: 10.5281/zenodo.4066748
title: Meteor-ablated Aluminum in the Mesosphere-Lower Thermosphere

description: Meteor-ablated Aluminum in the Mesosphere-Lower Thermosphere by John Plane, Shane Daly, Wuhu Feng, Michael Gerding and Juan Carlos Gómez Martín. The repository contains the data used in the above paper.
DOI: 10.5281/zenodo.4066747
title: Meteor-ablated Aluminum in the Mesosphere-Lower Thermosphere

description: Meteor-ablated Aluminum in the Mesosphere-Lower Thermosphere by John Plane, Shane Daly, Wuhu Feng, Michael Gerding and Juan Carlos Gómez Martín. The repository contains the data used in the above paper.
DOI: 10.21227/vvwg-hw82
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains IDs and sentiment scores of the geo-tagged tweets related to the COVID-19 pandemic. The tweets are captured by an on-going project deployed at https://live.rlamsal.com.np. The model monitors the real-time Twitter feed for coronavirus-related tweets using 90+ different keywords and hashtags that are commonly used while referencing the pandemic. Complying with Twitter's content redistribution policy, only the tweet IDs are shared. You can re-construct the dataset by hydrating these IDs. The tweet IDs in this dataset belong to the tweets tweeted providing an exact location.-------------------------------------Recently added: Coronavirus (COVID-19) Tweets Sentiment Trend (Global)-------------------------------------Below is the quick overview of this dataset.— Dataset name: GeoCOV19Tweets Dataset— Number of tweets : 238,272 tweets— Coverage : Global— Language : English (EN)— Primary dataset : Coronavirus (COVID-19) Tweets Dataset (COV19Tweets Dataset)— Dataset updates : Everyday— Usage policy : As per Twitter's Developer Policy— Active keywords and hashtags: keywords.tsvPlease visit this page (primary dataset) for details regarding the collection date and time of each CSV file present in this dataset.
DOI: 10.21227/75bf-8744
title: Coronavirus (COVID-19) Tweets Sentiment Trend (Global)

description: This dataset gives a cursory glimpse at the overall sentiment trend of the public discourse regarding the COVID-19 pandemic on Twitter. The live scatter plot of this dataset is available as The Overall Trend block at https://live.rlamsal.com.np. The trend graph reveals multiple peaks and drops that need further analysis. The n-grams during those peaks and drops can prove beneficial for better understanding the discourse.The dataset will be updated weekly and will continue until the development of the Coronavirus (COVID-19) Tweets Dataset is ongoing.A quick overview of the datasetThe sentiment scores are defined in the range [-1,0), 0, and (0,+1] for negative sentiment, neutral sentiment, and positive sentiment. Since the number of negative sentiment tweets is always less than the combined number of neutral and positive sentiment tweets, the majority of the time, the average sentiment falls pretty close to +0.05. So if we consider +0.05 as the neutral point for average sentiment score, then any score greater than +0.1 (peaks) and smaller than 0 (drops) can be regarded as a point of interest for further scrutinizing. Following are the dates when the Twitter stream (based on the tweets present in the Coronavirus (COVID-19) Tweets Dataset) experienced those peaks and drops:Positive peaks: In 2020 (April 30, May 3, May 23, May 24, May 25, May 26, June 2, June 22, June 28, July 3, July 12, July 26, August 15, August 16, August 18, August 21, August 24, August 31, September 1, September 2, September 4, September 5, September 9, September 21, September 23, October 2)Negative Peaks: In 2020 (May 28, May 30, May 31, June 1, June 2, June 7, June 8, June 12, June 13, June 14, June 15, June 21, June 24, june 25, July 6, July 7, July 10, August 26, September 1, September 3, September 13, September 17, September 25, September 26, September 28, October 5)What's inside the dataset files?Tweets collected every 10 minutes are sampled together, and an average sentiment score is computed. This dataset contains TXT files, each with two columns: (i) date/time (in UTC) and (ii) average sentiment. The first column is date/time and is by default in Unix timestamp (in ms). You can use this formula =cell/1000/60/60/24 + DATE(1970,1,1) in Spreadsheets, or this pd.to_datetime(dataframe_name[column],unit='ms') if you're comfortable with Python, to convert the Unix timestamp to human-readable format. There are 44 instances where the average sentiment score is NULL because of networking issues with the cloud service.
DOI: 10.5281/zenodo.4059331
title: longavailable/ydm-population-gdp-db

description: Population and gdp for cities in YDM
DOI: 10.6084/m9.figshare.13065170
title: Supplementary Material for: MYC Upregulation Confers Resistance to Everolimus and Establishes Vulnerability to Cyclin-Dependent Kinase Inhibitors in Pancreatic Neuroendocrine Neoplasm Cells

description: <b><i>Introduction:</i></b> Dysregulation of the mechanistic target of rapamycin complex 1 (mTORC1)-dependent pathways in pancreatic neuroendocrine neoplasms (PanNENs) underlies the introduction of the mTORC1 inhibitor everolimus as treatment of advanced progressive PanNENs. Although everolimus significantly increases progression-free survival, most patients acquire secondary resistance to the drug. This study aimed at identifying mechanisms involved in acquisition of resistance to everolimus. <b><i>Methods:</i></b> BON-1 and everolimus-resistant (ER) BON-1 cells were used as in vitro system of sensitivity and acquired resistance. Transcriptome changes occurring in BON-1 and ER-BON-1 were investigated by RNA sequencing and validated by quantitative PCR analysis. RNA extracted from patients’ biopsies was used to validate MYC upregulation. Drug screening and functional assays were performed using ER-BON-1 cells. Cell cycle progression was evaluated by FACS analysis. <b><i>Results:</i></b> Our results show that MYC overexpression is a key event in the development of secondary resistance to everolimus in PanNEN cell lines and in metastatic lesions from neuroendocrine neoplasm patients. MYC knockdown restored ER-BON-1 sensitivity to everolimus. Pharmacological inhibition of MYC mediated by the cyclin-dependent kinase inhibitor dinaciclib strongly reduced viability of ER-BON-1. Dinaciclib synergized with everolimus and inhibited ER-BON-1 cell cycle progression. <b><i>Discussion:</i></b> Our findings suggest that MYC upregulation drives the development of secondary resistance to everolimus in PanNENs and that its inhibition is an exploitable vulnerability. Indeed, our results indicate that combined treatments with cyclin-dependent kinase and mTOR inhibitors may counteract secondary resistance to everolimus in PanNENs and may pave the ground for new therapeutic regimens for these tumors.
DOI: 10.6084/m9.figshare.13065170.v1
title: Supplementary Material for: MYC Upregulation Confers Resistance to Everolimus and Establishes Vulnerability to Cyclin-Dependent Kinase Inhibitors in Pancreatic Neuroendocrine Neoplasm Cells

description: <b><i>Introduction:</i></b> Dysregulation of the mechanistic target of rapamycin complex 1 (mTORC1)-dependent pathways in pancreatic neuroendocrine neoplasms (PanNENs) underlies the introduction of the mTORC1 inhibitor everolimus as treatment of advanced progressive PanNENs. Although everolimus significantly increases progression-free survival, most patients acquire secondary resistance to the drug. This study aimed at identifying mechanisms involved in acquisition of resistance to everolimus. <b><i>Methods:</i></b> BON-1 and everolimus-resistant (ER) BON-1 cells were used as in vitro system of sensitivity and acquired resistance. Transcriptome changes occurring in BON-1 and ER-BON-1 were investigated by RNA sequencing and validated by quantitative PCR analysis. RNA extracted from patients’ biopsies was used to validate MYC upregulation. Drug screening and functional assays were performed using ER-BON-1 cells. Cell cycle progression was evaluated by FACS analysis. <b><i>Results:</i></b> Our results show that MYC overexpression is a key event in the development of secondary resistance to everolimus in PanNEN cell lines and in metastatic lesions from neuroendocrine neoplasm patients. MYC knockdown restored ER-BON-1 sensitivity to everolimus. Pharmacological inhibition of MYC mediated by the cyclin-dependent kinase inhibitor dinaciclib strongly reduced viability of ER-BON-1. Dinaciclib synergized with everolimus and inhibited ER-BON-1 cell cycle progression. <b><i>Discussion:</i></b> Our findings suggest that MYC upregulation drives the development of secondary resistance to everolimus in PanNENs and that its inhibition is an exploitable vulnerability. Indeed, our results indicate that combined treatments with cyclin-dependent kinase and mTOR inhibitors may counteract secondary resistance to everolimus in PanNENs and may pave the ground for new therapeutic regimens for these tumors.
DOI: 10.5281/zenodo.4072653
title: Test

description: Test data set.
DOI: 10.6084/m9.figshare.12251285
title: Data of the skeleton structure of vertebrae and caudal of mullets (Mugilidae).xlsx

description: The skeleton structure of vertebrae and caudal of mullets (Mugilidae)
DOI: 10.5281/zenodo.3935808
title: Endocrine disruption: the noise in available data adversely impact the models' performance

description: This paper is devoted to the analysis of available experimental data and preparation of predictive models for binding affinity of molecules with respect to two nuclear receptors involved in endocrine disruption: the Estrogen (ER) and the Androgen (AR) receptor. The ED-relevant data were retrieved from multiple sources, including the CERAP, CoMPARA, and the Tox21 data challenge projects as well as ChEMBL and PubChem databases. Data analysis performed with the help of Generative Topographic Mapping technique revealed the problem of a low agreement between experimental values issued from different sources. Collected data were used to train both classification models for AR and ER binding activities and regression models for Relative Binding Affinity (RBA) and median Inhibition Concentration (IC50) models. These models displayed relatively poor performance in classification (sensitivities ER = 0.34, AR = 0.49) and in regression (determination coefficient R<sup>2</sup> for the RBA and IC50 models in external validation varied from 0.44 to 0.76). Our analysis demonstrates that low models performances resulted from misinterpreted experimental endpoints or wrongly reported values. Developed models and collected data sets included of 6215 (ER) and 3789 (AR) unique compounds; they are freely available. The repository includes data on estrogen and androgen receptor binding behavior (binder, non-binder), median inhibitory concentration (IC50) and relative binding affinity (RBA). <strong>SDF fields:</strong> <em>DB</em> = database; where: COMPARA = Collaborative Modelling Project for Androgen Receptor Activity; CERAPP = Collaborative Estrogen Receptor Activity Prediction Project; Tox-DC = data from Tox21 program; PubChem = data from PubChem. <em>Set</em> = whether the compound was used in training or test set for the given model <em>Receptor</em> = AR stands for Androgen Receptor and ER stands for Estrogen Receptor <em>binding_prp</em> = binding behaviour for the classification model (ER and AR). 1 = binder; = non-binder <em>IC50 (nM) </em>and <em>logIC50</em> = median inhibitory concentration values in nanoMolar and log. <em>RBA(%) </em>and <em>logRBA</em> = relative binding affinity values in % and log.
DOI: 10.5281/zenodo.3935807
title: Endocrine disruption: the noise in available data adversely impact the models' performance

description: This paper is devoted to the analysis of available experimental data and preparation of predictive models for binding affinity of molecules with respect to two nuclear receptors involved in endocrine disruption: the Estrogen (ER) and the Androgen (AR) receptor. The ED-relevant data were retrieved from multiple sources, including the CERAP, CoMPARA, and the Tox21 data challenge projects as well as ChEMBL and PubChem databases. Data analysis performed with the help of Generative Topographic Mapping technique revealed the problem of a low agreement between experimental values issued from different sources. Collected data were used to train both classification models for AR and ER binding activities and regression models for Relative Binding Affinity (RBA) and median Inhibition Concentration (IC50) models. These models displayed relatively poor performance in classification (sensitivities ER = 0.34, AR = 0.49) and in regression (determination coefficient R<sup>2</sup> for the RBA and IC50 models in external validation varied from 0.44 to 0.76). Our analysis demonstrates that low models performances resulted from misinterpreted experimental endpoints or wrongly reported values. Developed models and collected data sets included of 6215 (ER) and 3789 (AR) unique compounds; they are freely available. The repository includes data on estrogen and androgen receptor binding behavior (binder, non-binder), median inhibitory concentration (IC50) and relative binding affinity (RBA). <strong>SDF fields:</strong> <em>DB</em> = database; where: COMPARA = Collaborative Modelling Project for Androgen Receptor Activity; CERAPP = Collaborative Estrogen Receptor Activity Prediction Project; Tox-DC = data from Tox21 program; PubChem = data from PubChem. <em>Set</em> = whether the compound was used in training or test set for the given model <em>Receptor</em> = AR stands for Androgen Receptor and ER stands for Estrogen Receptor <em>binding_prp</em> = binding behaviour for the classification model (ER and AR). 1 = binder; = non-binder <em>IC50 (nM) </em>and <em>logIC50</em> = median inhibitory concentration values in nanoMolar and log. <em>RBA(%) </em>and <em>logRBA</em> = relative binding affinity values in % and log.
DOI: 10.6084/m9.figshare.13065254
title: Supplementary Material for: Occurrence of Cerebrovascular Diseases Decreased after the Great East Japan Earthquake and Tsunami of 2011

description: <b><i>Background:</i></b> A temporary increase in the occurrence of cerebrovascular diseases (CVDs) after the Great East Japan Earthquake and Tsunami of 2011 was reported; however, no studies have been conducted to investigate long-term effects. We assessed the long-term impact of the disaster on the incidence of CVDs. <b><i>Methods:</i></b> Incidence data for CVDs from 2008 to 2017 were acquired from the population-based Stroke Registry with an inventory survey of Iwate Prefecture, Japan. Part of the coastal area in Iwate Prefecture was mildly flooded and the other part was severely flooded. Age-adjusted incidence rates of CVDs (according to the Japanese standard population) were calculated for each area. The relative risk (RR) of incidence based on the years before the disaster (2008–2010), adjusted by stratified age groups, was calculated for the year of the disaster (2011), and the years after the disaster (2012–2017) in each area. <b><i>Results:</i></b> The age-adjusted incidence rates gradually decreased in all areas, with the exception of a temporary increase among men who lived on the coast the year the disaster occurred. The adjusted RR in the disaster year were not significant in any area and those of the postdisaster years were 0.91 (95% CI 0.87–0.96) for all inland men, 0.93 (0.89–0.97) for all inland women, 0.85 (0.78–0.93) for all coastal men, 0.87 (0.81–0.94) for all coastal women, 0.88 (0.80–0.98) for men at mildly flooded coast, 0.82 (0.75–0.89) for women at mildly flooded coast, 0.79 (0.68–0.91) for men at severely flooded coast, and 0.98 (0.86–1.11) for women at severely flooded coast. <b><i>Conclusions:</i></b> The occurrence of CVDs in the flooded coastal areas did not increase in the year of the Great East Japan Earthquake and Tsunami; furthermore, it decreased for men according to the severity of flood damage in the subsequent years; this can be attributed to supportive activities for the tsunami victims and the migration of the population.
DOI: 10.5517/ccdc.csd.cc25j3nh
title: CCDC 2011486: Experimental Crystal Structure Determination

description: Related Article: Pokpong Rungthanaphatsophon, Alexander J. Gremillion, Yuzhong Wang, Steven P. Kelley, Gregory H. Robinson, Justin R. Walensky|2021|Inorg.Chim.Acta|514|120033|doi:10.1016/j.ica.2020.120033
DOI: 10.5517/ccdc.csd.cc25j3pj
title: CCDC 2011487: Experimental Crystal Structure Determination

description: Related Article: Pokpong Rungthanaphatsophon, Alexander J. Gremillion, Yuzhong Wang, Steven P. Kelley, Gregory H. Robinson, Justin R. Walensky|2021|Inorg.Chim.Acta|514|120033|doi:10.1016/j.ica.2020.120033
DOI: 10.5517/ccdc.csd.cc25j3rl
title: CCDC 2011489: Experimental Crystal Structure Determination

description: Related Article: Pokpong Rungthanaphatsophon, Alexander J. Gremillion, Yuzhong Wang, Steven P. Kelley, Gregory H. Robinson, Justin R. Walensky|2021|Inorg.Chim.Acta|514|120033|doi:10.1016/j.ica.2020.120033
DOI: 10.5281/zenodo.4072712
title: Data set: Burn severity and soil chemistry are weak drivers of early vegetation succession following a boreal mega-fire in a production forest landscape

description: Data used in the analyses of the study. Data set on soil chemistry (pH, organic content, nutrient content), plots distance to the fire perimeter, and plant communities (vascular plants and bryophytes) recorded in permanent plots on clearcuts in a burn-severity gradient two and five years following a wildfire. Data collected from a 13000 hectare wildfire that occurred in 2014, in boreal southern Sweden. Calculated trait means from data bases, and plant community data from unburned plots from another study, are are also included here.