cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25345/c5447s
title: MassIVE MSV000086515 - The adipose microenvironment dysregulates the mammary myoepithelial cells and could participate to the progression of breast cancer
title: Overweight and obesity are now recognized as established risk factors for breast cancer in postmenopausal women. Reciprocal interactions have been described between adipose and cancer cells. Among the cell types present in the breast, myoepithelial cells (MECs) are considered "tumour suppressor" cells. During the transition from ductal carcinoma in situ to invasive cancer, disorganization or even the disappearance of MECs is observed. As the adipose microenvironment is now considered as a central actor in the progression of breast cancer, our objective was to evaluate if it could be involved in MEC functional modifications, leading to the transition of in situ to invasive carcinoma, particularly in obese patients. Through a co-culture model, we found that adipose cells could decrease the viability of the MECs. The adipose cells could also disrupt the expression of the genes involved in the maintenance of the extracellular matrix and to amplify the expression of leptin and inflammatory markers. The metabolite analyses revealed specific profiles that may be involved in the growth of neoplastic cells. All of these perturbations could thus be responsible for the loss of tumour suppressor status of MECs and promote the transition from in situ to invasive carcinoma.
DOI: 10.15468/x5wcap
title: New species of Duplominona Karling, 1966 (Platyhelminthes, Proseriata) from the Pacific coast of Panama

description: This dataset contains the digitized treatments in Plazi based on the original journal article Curini-Galletti, Marco, Carcupino, Marcella, Stocchino, Giacinta A., Leasi, Francesca, Norenburg, Jon L. (2020): New species of Duplominona Karling, 1966 (Platyhelminthes, Proseriata) from the Pacific coast of Panama. Zootaxa 4881 (3): 482-498, DOI: https://doi.org/10.11646/zootaxa.4881.3.3
DOI: 10.17632/sjmxfk64r8.1
title: Data for: The Modelling of Surface-Water Photoreactions Made Easier: Introducing the Concept of 'Equivalent Monochromatic Wavelengths'
DOI: 10.5281/zenodo.4289234
title: Variational Design Principles For Nonequilibrium Colloidal Assembly

description: Data required to reproduce plotted figures in arXiv preprint: arXiv:2011.10592 (2020)
DOI: 10.5519/qd.1i5ibjyi
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (834573 records)
DOI: 10.15468/pkaf39
title: Redescription of a poorly known upogebiid mud shrimp Gebiacantha acanthochela (Sakai, 1967) and description of a new species of the genus from the Ryukyu Islands, Japan (Decapoda: Gebiidea)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Komai, Tomoyuki (2020): Redescription of a poorly known upogebiid mud shrimp Gebiacantha acanthochela (Sakai, 1967) and description of a new species of the genus from the Ryukyu Islands, Japan (Decapoda: Gebiidea). Zootaxa 4881 (2): 307-322, DOI: https://doi.org/10.11646/zootaxa.4881.2.5
DOI: 10.6084/m9.figshare.13283795.v1
title: COVID-19 Data for the first wave

description: We collected county-level cumulative COVID-19 confirmed cases and death from Mar 25 to Nov 12, 2020, across the contiguous United States from <i>USAFacts</i> (usafacts.org). We considered Mar 25 to Jun 3 as the “1<sup>st</sup> wave”, Jun 4 to Sep 2 as the “2<sup>nd</sup> wave”, and Sep 3 to Nov 12 as the “3<sup>rd</sup> wave” of COVID-19. For the 2<sup>nd</sup> and 3<sup>rd</sup> waves, we analyzed the targeted counties in the sunbelt region (including AL, AZ, AR, CA, FL, GA, KS, LA, MS, NV, NM, NC, OK, SC, TX, TN, and UT states) and great plains region (including IA, IL, IN, KS, MI, MO, MN, ND, NE, OH, SD, and WI states), respectively. MIR, as a proxy for survival rate, is calculated by dividing the number of confirmed deaths in each county by the confirmed cases in the same county at the same time-period multiplied by 100. MIR ranges from 0%-100%, 100% indicating the worst situation where all confirmed cases have died. Thirty-eight potential risk factors (covariates), including county-level MR of comorbidities & disorders, demographics & social factors, and environmental factors, were retrieved from the <i>University of Washington Global Health Data Exchange</i> (http://ghdx.healthdata.org/us-data). Comorbidities and disorders include CVD, cardiomyopathy and myocarditis and myocarditis, hypertensive heart disease, peripheral vascular disease, atrial fibrillation, cerebrovascular disease, diabetes, hepatitis, HIV/AIDS, tuberculosis (TB), lower respiratory infection, interstitial lung disease and pulmonary sarcoidosis, asthma, COPD, ischemia, mesothelioma, tracheal cancer, leukemia, pancreatic cancer, rheumatic disease, drug use disorder, and alcohol use disorder. Demographics & social factors include age, female African American%, female white American%, male African American%, male white American%, Asian%, smokers%, unemployed%, income rate, food insecurity, fair/poor health, and uninsured%. Environmental factors include county population density, air quality index (AQI), temperature, and PM. A descriptive table, including all potential risk factors, is provided in Table S1).<br>
DOI: 10.6084/m9.figshare.13283795
title: COVID-19 Data for the first wave

description: We collected county-level cumulative COVID-19 confirmed cases and death from Mar 25 to Nov 12, 2020, across the contiguous United States from <i>USAFacts</i> (usafacts.org). We considered Mar 25 to Jun 3 as the “1<sup>st</sup> wave”, Jun 4 to Sep 2 as the “2<sup>nd</sup> wave”, and Sep 3 to Nov 12 as the “3<sup>rd</sup> wave” of COVID-19. For the 2<sup>nd</sup> and 3<sup>rd</sup> waves, we analyzed the targeted counties in the sunbelt region (including AL, AZ, AR, CA, FL, GA, KS, LA, MS, NV, NM, NC, OK, SC, TX, TN, and UT states) and great plains region (including IA, IL, IN, KS, MI, MO, MN, ND, NE, OH, SD, and WI states), respectively. MIR, as a proxy for survival rate, is calculated by dividing the number of confirmed deaths in each county by the confirmed cases in the same county at the same time-period multiplied by 100. MIR ranges from 0%-100%, 100% indicating the worst situation where all confirmed cases have died. Thirty-eight potential risk factors (covariates), including county-level MR of comorbidities & disorders, demographics & social factors, and environmental factors, were retrieved from the <i>University of Washington Global Health Data Exchange</i> (http://ghdx.healthdata.org/us-data). Comorbidities and disorders include CVD, cardiomyopathy and myocarditis and myocarditis, hypertensive heart disease, peripheral vascular disease, atrial fibrillation, cerebrovascular disease, diabetes, hepatitis, HIV/AIDS, tuberculosis (TB), lower respiratory infection, interstitial lung disease and pulmonary sarcoidosis, asthma, COPD, ischemia, mesothelioma, tracheal cancer, leukemia, pancreatic cancer, rheumatic disease, drug use disorder, and alcohol use disorder. Demographics & social factors include age, female African American%, female white American%, male African American%, male white American%, Asian%, smokers%, unemployed%, income rate, food insecurity, fair/poor health, and uninsured%. Environmental factors include county population density, air quality index (AQI), temperature, and PM. A descriptive table, including all potential risk factors, is provided in Table S1).<br>
DOI: 10.6084/m9.figshare.13283801
title: COVID-19 data for the second wave

description: We collected county-level cumulative COVID-19 confirmed cases and death from Mar 25 to Nov 12, 2020, across the contiguous United States from <i>USAFacts</i> (usafacts.org). We considered Mar 25 to Jun 3 as the “1<sup>st</sup> wave”, Jun 4 to Sep 2 as the “2<sup>nd</sup> wave”, and Sep 3 to Nov 12 as the “3<sup>rd</sup> wave” of COVID-19. For the 2<sup>nd</sup> and 3<sup>rd</sup> waves, we analyzed the targeted counties in the sunbelt region (including AL, AZ, AR, CA, FL, GA, KS, LA, MS, NV, NM, NC, OK, SC, TX, TN, and UT states) and great plains region (including IA, IL, IN, KS, MI, MO, MN, ND, NE, OH, SD, and WI states), respectively. MIR, as a proxy for survival rate, is calculated by dividing the number of confirmed deaths in each county by the confirmed cases in the same county at the same time-period multiplied by 100. MIR ranges from 0%-100%, 100% indicating the worst situation where all confirmed cases have died.<br> Thirty-eight potential risk factors (covariates), including county-level MR of comorbidities & disorders, demographics & social factors, and environmental factors, were retrieved from the <i>University of Washington Global Health Data Exchange</i> (http://ghdx.healthdata.org/us-data). Comorbidities and disorders include CVD, cardiomyopathy and myocarditis and myocarditis, hypertensive heart disease, peripheral vascular disease, atrial fibrillation, cerebrovascular disease, diabetes, hepatitis, HIV/AIDS, tuberculosis (TB), lower respiratory infection, interstitial lung disease and pulmonary sarcoidosis, asthma, COPD, ischemia, mesothelioma, tracheal cancer, leukemia, pancreatic cancer, rheumatic disease, drug use disorder, and alcohol use disorder. Demographics & social factors include age, female African American%, female white American%, male African American%, male white American%, Asian%, smokers%, unemployed%, income rate, food insecurity, fair/poor health, and uninsured%. Environmental factors include county population density, air quality index (AQI), temperature, and PM. A descriptive table, including all potential risk factors, is provided in Table S1).
DOI: 10.6084/m9.figshare.13283801.v1
title: COVID-19 data for the second wave

description: We collected county-level cumulative COVID-19 confirmed cases and death from Mar 25 to Nov 12, 2020, across the contiguous United States from <i>USAFacts</i> (usafacts.org). We considered Mar 25 to Jun 3 as the “1<sup>st</sup> wave”, Jun 4 to Sep 2 as the “2<sup>nd</sup> wave”, and Sep 3 to Nov 12 as the “3<sup>rd</sup> wave” of COVID-19. For the 2<sup>nd</sup> and 3<sup>rd</sup> waves, we analyzed the targeted counties in the sunbelt region (including AL, AZ, AR, CA, FL, GA, KS, LA, MS, NV, NM, NC, OK, SC, TX, TN, and UT states) and great plains region (including IA, IL, IN, KS, MI, MO, MN, ND, NE, OH, SD, and WI states), respectively. MIR, as a proxy for survival rate, is calculated by dividing the number of confirmed deaths in each county by the confirmed cases in the same county at the same time-period multiplied by 100. MIR ranges from 0%-100%, 100% indicating the worst situation where all confirmed cases have died.<br> Thirty-eight potential risk factors (covariates), including county-level MR of comorbidities & disorders, demographics & social factors, and environmental factors, were retrieved from the <i>University of Washington Global Health Data Exchange</i> (http://ghdx.healthdata.org/us-data). Comorbidities and disorders include CVD, cardiomyopathy and myocarditis and myocarditis, hypertensive heart disease, peripheral vascular disease, atrial fibrillation, cerebrovascular disease, diabetes, hepatitis, HIV/AIDS, tuberculosis (TB), lower respiratory infection, interstitial lung disease and pulmonary sarcoidosis, asthma, COPD, ischemia, mesothelioma, tracheal cancer, leukemia, pancreatic cancer, rheumatic disease, drug use disorder, and alcohol use disorder. Demographics & social factors include age, female African American%, female white American%, male African American%, male white American%, Asian%, smokers%, unemployed%, income rate, food insecurity, fair/poor health, and uninsured%. Environmental factors include county population density, air quality index (AQI), temperature, and PM. A descriptive table, including all potential risk factors, is provided in Table S1).
DOI: 10.6084/m9.figshare.13283810.v1
title: COVID-19 data for the third wave

description: We collected county-level cumulative COVID-19 confirmed cases and death from Mar 25 to Nov 12, 2020, across the contiguous United States from <i>USAFacts</i> (usafacts.org). We considered Mar 25 to Jun 3 as the “1<sup>st</sup> wave”, Jun 4 to Sep 2 as the “2<sup>nd</sup> wave”, and Sep 3 to Nov 12 as the “3<sup>rd</sup> wave” of COVID-19. For the 2<sup>nd</sup> and 3<sup>rd</sup> waves, we analyzed the targeted counties in the sunbelt region (including AL, AZ, AR, CA, FL, GA, KS, LA, MS, NV, NM, NC, OK, SC, TX, TN, and UT states) and great plains region (including IA, IL, IN, KS, MI, MO, MN, ND, NE, OH, SD, and WI states), respectively. MIR, as a proxy for survival rate, is calculated by dividing the number of confirmed deaths in each county by the confirmed cases in the same county at the same time-period multiplied by 100. MIR ranges from 0%-100%, 100% indicating the worst situation where all confirmed cases have died.<br> Thirty-eight potential risk factors (covariates), including county-level MR of comorbidities & disorders, demographics & social factors, and environmental factors, were retrieved from the <i>University of Washington Global Health Data Exchange</i> (http://ghdx.healthdata.org/us-data). Comorbidities and disorders include CVD, cardiomyopathy and myocarditis and myocarditis, hypertensive heart disease, peripheral vascular disease, atrial fibrillation, cerebrovascular disease, diabetes, hepatitis, HIV/AIDS, tuberculosis (TB), lower respiratory infection, interstitial lung disease and pulmonary sarcoidosis, asthma, COPD, ischemia, mesothelioma, tracheal cancer, leukemia, pancreatic cancer, rheumatic disease, drug use disorder, and alcohol use disorder. Demographics & social factors include age, female African American%, female white American%, male African American%, male white American%, Asian%, smokers%, unemployed%, income rate, food insecurity, fair/poor health, and uninsured%. Environmental factors include county population density, air quality index (AQI), temperature, and PM. A descriptive table, including all potential risk factors, is provided in Table S1).
DOI: 10.6084/m9.figshare.13283810
title: COVID-19 data for the third wave

description: We collected county-level cumulative COVID-19 confirmed cases and death from Mar 25 to Nov 12, 2020, across the contiguous United States from <i>USAFacts</i> (usafacts.org). We considered Mar 25 to Jun 3 as the “1<sup>st</sup> wave”, Jun 4 to Sep 2 as the “2<sup>nd</sup> wave”, and Sep 3 to Nov 12 as the “3<sup>rd</sup> wave” of COVID-19. For the 2<sup>nd</sup> and 3<sup>rd</sup> waves, we analyzed the targeted counties in the sunbelt region (including AL, AZ, AR, CA, FL, GA, KS, LA, MS, NV, NM, NC, OK, SC, TX, TN, and UT states) and great plains region (including IA, IL, IN, KS, MI, MO, MN, ND, NE, OH, SD, and WI states), respectively. MIR, as a proxy for survival rate, is calculated by dividing the number of confirmed deaths in each county by the confirmed cases in the same county at the same time-period multiplied by 100. MIR ranges from 0%-100%, 100% indicating the worst situation where all confirmed cases have died.<br> Thirty-eight potential risk factors (covariates), including county-level MR of comorbidities & disorders, demographics & social factors, and environmental factors, were retrieved from the <i>University of Washington Global Health Data Exchange</i> (http://ghdx.healthdata.org/us-data). Comorbidities and disorders include CVD, cardiomyopathy and myocarditis and myocarditis, hypertensive heart disease, peripheral vascular disease, atrial fibrillation, cerebrovascular disease, diabetes, hepatitis, HIV/AIDS, tuberculosis (TB), lower respiratory infection, interstitial lung disease and pulmonary sarcoidosis, asthma, COPD, ischemia, mesothelioma, tracheal cancer, leukemia, pancreatic cancer, rheumatic disease, drug use disorder, and alcohol use disorder. Demographics & social factors include age, female African American%, female white American%, male African American%, male white American%, Asian%, smokers%, unemployed%, income rate, food insecurity, fair/poor health, and uninsured%. Environmental factors include county population density, air quality index (AQI), temperature, and PM. A descriptive table, including all potential risk factors, is provided in Table S1).
DOI: 10.35002/xy93-dw49
title: Canada’s Energy Future 2020: Energy Supply and Demand Projections to 2050
DOI: 10.5281/zenodo.4289269
title: Simple Dataset from 1 to 10

description: This is a simple dataset from 1 to 10 updated This is more description to the second version This is further description
DOI: 10.5281/zenodo.4287706
title: Simple Dataset from 1 to 10

description: This is a simple dataset from 1 to 10 updated This is more description to the second version This is further description
DOI: 10.5519/qd.o4yeac3n
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1347743 records)
DOI: 10.7910/dvn/g1g9td
title: Texas County-Level Data for 2006, 2008, and 2018

description: Texas County-Level Data for 2006, 2008, and 2018: Oil and Gas Production, All Vehicle Crash Statistics, HIV
DOI: 10.5519/qd.4zrjy7bj
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.6084/m9.figshare.9864677
title: 3-Dimensional Sheep Femur Bone Trabecular

description: A dataset of 40 three-dimensional bone images of a sheep femur in the nifti file format (.nii) with the image size of 100 x 100 x 100 voxels and the voxel size of 20 x 20 x 20 µm
DOI: 10.5281/zenodo.4289359
title: An interview with Andrew Bentley

description: This publication consists of an email discussion with Andrew Bentley and Mikko Ojanen during ##.##.2018 and ##.##. 2019.
DOI: 10.5281/zenodo.4289358
title: An interview with Andrew Bentley

description: This publication consists of an email discussion with Andrew Bentley and Mikko Ojanen during ##.##.2018 and ##.##. 2019.
DOI: 10.5281/zenodo.4271168
title: An interview with Leo Nilsson

description: This publication consists of an email discussion with Leo Nilsson and Mikko Ojanen during ##.##.2019 and ##.##2020.
DOI: 10.5281/zenodo.4271167
title: An interview with Leo Nilsson

description: This publication consists of an email discussion with Leo Nilsson and Mikko Ojanen during ##.##.2019 and ##.##2020.
DOI: 10.6084/m9.figshare.3761562.v1689
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.17632/tjnvtmy3n2.1
title: Data for: Preliminary Feasibility Study of PCM condenser for PCCS of APR1400

description: These are results from the Phase Change Material (PCM) heat absorption experiment.
DOI: 10.6084/m9.figshare.13150487
title: Supplemental Material for Albuquerque et al. 2020

description: Table S1 - Excel file of final dataset used for analyses, including, body mass, body temperature during V̇O<sub>2</sub>max trials, V̇O<sub>2</sub>max in ml per hour.Table S2 - The phylogenetic tree is also available as a text file<br>in NEXUS format that can be opened in any text editor, Mesquite, R or various other free software programs, and as a DSC file (Table S3).
DOI: 10.6084/m9.figshare.13150487.v1
title: TableS1_aaVO2MAX_22_RA_20_JustLizardSpeciesAverages_42_ForMatLab.xlsx

description: Table S1 - Excel file of final dataset used for analyses, including, body mass, body temperature during V̇O<sub>2</sub>max trials, V̇O<sub>2</sub>max in ml per hour.
DOI: 10.25897/5/kk8s-7a12
title: Mobilisation of data for GFBio services - Bacterial Descriptions Use Case

description: The description of five comprehensively annotated bacterial strains have been imported into the DiversityDescriptions in the course of the project GFBio (www.gfbio.org). The purpose of this mobilization is the availability of taxon-related descriptive information for the GFBio data portal following various standard schemas, e.g. SDD, Delta for the test and development of data pipelines and work flows.
The data has been manually annotated out of species descriptions derived by results of recently completed and ongoing projects carried out and published by researchers at the DSMZ. The ecological and environmental relevance of these strains is given by their participation in nitrogen and carbon cycles (significant exoenzyme activity) within their particular habitat. The detailed documented metabolic and physiologic profile of each strain indicates their significant adaptation to the soils of different climates. Hence, these strains are important biotic components of their environment.
Besides the availability of these data sets within DiversityDescriptions, the detailed descriptions can also be retrieved from the BacDive - The Bacterial Diversity Metadatabase ( http://bacdive.dsmz.de) portal:

        Blastocatella fastidiosa Fösel et al. 2013 A2_16 (-> http://bacdive.dsmz.de/index.php?site=search&rd=23454)
        Aridibacter kavangonensis Huber et al. 2014 Ac_23_E3 (-> http://bacdive.dsmz.de/index.php?search=24777)
        Aridibacter famidurans Huber et al. 2014 A22_HD_4H (-> http://bacdive.dsmz.de/index.php?site=search&rd=24776)
        Edaphobacter aggregans Koch et al. 2008 emend. Dedysh et al. 2012 WBG 1 (-> http://bacdive.dsmz.de/index.php?site=search&rd=133)
        Edaphobacter modestus Koch et al. 2008 JBG-1 (-> http://bacdive.dsmz.de/index.php?site=search&rd=132)

When using BacDive content please consider citing the following paper:

BacDive - the Bacterial Diversity Metadatabase
Söhngen C., Bunk B., Podstawka A., Gleim D., Overmann J.
Nucleic Acids Res. 2014 Jan 1;42(1):D592-9. doi: 10.1093/nar/gkt1058. Epub 2013 Nov 7.
PMID:  24214959
DOI: 10.25897/5/tyc9-k378
title: Mobilisation of data for GFBio services - Bacterial Descriptions Use Case

description: The description of five comprehensively annotated bacterial strains have been imported into the DiversityDescriptions in the course of the project GFBio (www.gfbio.org). The purpose of this mobilization is the availability of taxon-related descriptive information for the GFBio data portal following various standard schemas, e.g. SDD, Delta for the test and development of data pipelines and work flows.
The data has been manually annotated out of species descriptions derived by results of recently completed and ongoing projects carried out and published by researchers at the DSMZ. The ecological and environmental relevance of these strains is given by their participation in nitrogen and carbon cycles (significant exoenzyme activity) within their particular habitat. The detailed documented metabolic and physiologic profile of each strain indicates their significant adaptation to the soils of different climates. Hence, these strains are important biotic components of their environment.
Besides the availability of these data sets within DiversityDescriptions, the detailed descriptions can also be retrieved from the BacDive - The Bacterial Diversity Metadatabase ( http://bacdive.dsmz.de) portal:

        Blastocatella fastidiosa Fösel et al. 2013 A2_16 (-> http://bacdive.dsmz.de/index.php?site=search&rd=23454)
        Aridibacter kavangonensis Huber et al. 2014 Ac_23_E3 (-> http://bacdive.dsmz.de/index.php?search=24777)
        Aridibacter famidurans Huber et al. 2014 A22_HD_4H (-> http://bacdive.dsmz.de/index.php?site=search&rd=24776)
        Edaphobacter aggregans Koch et al. 2008 emend. Dedysh et al. 2012 WBG 1 (-> http://bacdive.dsmz.de/index.php?site=search&rd=133)
        Edaphobacter modestus Koch et al. 2008 JBG-1 (-> http://bacdive.dsmz.de/index.php?site=search&rd=132)

When using BacDive content please consider citing the following paper:

BacDive - the Bacterial Diversity Metadatabase
Söhngen C., Bunk B., Podstawka A., Gleim D., Overmann J.
Nucleic Acids Res. 2014 Jan 1;42(1):D592-9. doi: 10.1093/nar/gkt1058. Epub 2013 Nov 7.
PMID:  24214959
DOI: 10.6084/m9.figshare.12888008
title: Buoy Seno Ballena

description: 10 months (2018) time series data of pCO<sub>2</sub>, pH, temperature, salinity, and dissolved oxygen at Seno Ballena fjord in the Chilean Patagonia.The sensors (SAMI-CO<sub>2</sub>, SAMI-pH, SBE37, Aanderaa 5331A) recorded data every 4 hour from March to December. Lat 53.67461 S, Long 72.56061 W.