cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.13087442.v2
title: Dagnino et al 2020 Kinin and reserpine.pzfx

description: Fibromyalgia-like models in mice induced by reserpine opened a new avenue to better understand the molecular mechanism behind this complex better and incapacitate pain syndrome. Kinin B1 receptor (B1R) contributes to mechanical allodynia and acute coping behavior in mice under inflammatory and immunological conditions. The present study added new results that confirm a recently published study by Brusco et al. (2019). This study replicated our previous data where amine depletion induced by reserpine significantly decreased the dopamine and serotonin levels in prefrontal cortex (PFC), hippocampus (HPC), and spinal cord of mice. The animals submitted to the reserpine fibromyalgia model also showed a decrease of paw withdrawal threshold (PWT) and increased the immobility time in the forced swimming test (FST). The genetic deletion of B1R or pharmacological blockade by selective kinin B1R antagonist R-715 (acute i.p. treatment) counteracted the mechanical allodynia and increased immobility time induced by reserpine. However, neither pharmacological nor genetic inhibition of B1R reversed the monoamine depletion. Our data confirm that reserpine induced a fibromyalgia-like phenotype in mice and the role of B1R on the acute coping behavior and nociception modulation.
DOI: 10.6084/m9.figshare.13087442.v1
title: Dagnino et al 2020 Kinin and reserpine.pzfx

description: Fibromyalgia-like models in mice induced by reserpine opened a new avenue to better understand the molecular mechanism behind this complex better and incapacitate pain syndrome. Kinin B1 receptor (B1R) contributes to mechanical allodynia and acute coping behavior in mice under inflammatory and immunological conditions. The present study added new results that confirm a recently published study by Brusco et al. (2019). This study replicated our previous data where amine depletion induced by reserpine significantly decreased the dopamine and serotonin levels in prefrontal cortex (PFC), hippocampus (HPC), and spinal cord of mice. The animals submitted to the reserpine fibromyalgia model also showed a decrease of paw withdrawal threshold (PWT) and increased the immobility time in the forced swimming test (FST). The genetic deletion of B1R or pharmacological blockade by selective kinin B1R antagonist R-715 (acute i.p. treatment) counteracted the mechanical allodynia and increased immobility time induced by reserpine. However, neither pharmacological nor genetic inhibition of B1R reversed the monoamine depletion. Our data confirm that reserpine induced a fibromyalgia-like phenotype in mice and the role of B1R on the acute coping behavior and nociception modulation.
DOI: 10.5281/zenodo.4277905
title: Dataset: SODIS performance on the inactivation of E. coli under different irradiance and temperature conditions

description: SODIS performance on the inactivation of <em>E. coli</em> under different irradiance and temperature conditions. Experimental data for calibration of inactivation models.
DOI: 10.21227/p4xd-0b70
title: Two-Fold Reduction of Switching Current Density in Phase Change Memory Using Bi2Te3 Thermoelectric Interfacial Layer

description: This document shows time-domain thermoreflectance (TDTR) measurements on blanket phase change multilayers [Fig. S1] and electro-thermal simulations of confined PCM cells [Fig. S2]. The extracted thermal resistance per unit area obtained from our measured TDTR signal ratio vs. time delay in Fig. S1 reveals that Bi2Te3 layer only introduces ~13% additional thermal resistance in the PCM stack. On the other hand, Fig. S2 shows the electro-thermal simulation of confined cellswith 4 nm Bi2Te3 and 50 nm GST layer with a BE (TiN) diameter of 150 nm in Fig. S2(a) and control device with only 50 nm GST layer in Fig. S2(b). Simulations suggest that confined cell devices have ~20% lower Jreset compared to mushroom cell devices shown in the main text Fig. 4.
DOI: 10.25345/c5dr3z
title: MassIVE MSV000086470 - 7120-HGG1 - Dataset Creation from GNPS Molcular Networking - 8dfc4d60dd644e4a8b1060877fba3687
title: LC-MS & molecular network analysis of the model predator-prey system of the heterolobosean amoeba HGG1 predating on a cyanobacterial prey, the filamentous Anabaena sp. PCC 7120.
DOI: 10.21227/er00-t455
title: Two-Fold Reduction of Switching Current Density in Phase Change Memory Using Bi2Te3 Thermoelectric Interfacial Layer

description: This document shows time-domain thermoreflectance (TDTR) measurements on blanket phase change multilayers [Fig. S1] and electro-thermal simulations of confined PCM cells [Fig. S2]. The extracted thermal resistance per unit area obtained from our measured TDTR signal ratio vs. time delay in Fig. S1 reveals that Bi2Te3 layer only introduces ~13% additional thermal resistance in the PCM stack. On the other hand, Fig. S2 shows the electro-thermal simulation of confined cellswith 4 nm Bi2Te3 and 50 nm GST layer with a BE (TiN) diameter of 150 nm in Fig. S2(a) and control device with only 50 nm GST layer in Fig. S2(b). Simulations suggest that confined cell devices have ~20% lower Jreset compared to mushroom cell devices shown in the main text Fig. 4.
DOI: 10.25919/y1zx-1y57
title: Parkes observations for project P1068 semester 2020APRS_29

description: In the fast-moving world of Fast Radio Bursts (FRBs), the key question of what fraction (as high as 100%?) of FRBs emit repeat bursts remains unanswered. Of order 10 repeating FRBs are now known, and their spectro-temporal properties hint at differences with apparently non-repeating bursts - but poor statistics preclude a definitive diagnosis. FRB 180301 is a promising source for further study for multiple reasons: it is the first repeating FRB seen by the FAST telescope, and has the second-highest rotation measure ever seen for a FRB (after the original repeater, FRB121102). We request Parkes observations with the UWL to characterise the broadband emission properties of this FRB, with the following key goals: to constrain the repetition rate, identify any variation of pulse characteristics over time, and provide insight into the emission mechanisms. In its initial detection, FRB 180301 was poorly fit by regular Faraday rotation models, and showed significant circular polarization, which may indicate the progenitor is embedded in an extreme magneto-ionic environment with complex field structure that has not been seen before. These observations will generate a large data volume. We will use the Medusa backend in parallel with the Breakthrough Listen data recorder (BL), and will transfer data to BL immediately after observations.These data will be used by P. Kumar to prototype a FRB pipeline for the UWL, that will be made broadly available for similar studies.
DOI: 10.5281/zenodo.4277706
title: Simulation results for AGS paper

description: Simulation result data for the DrugLogics AGS paper (to be submitted)
DOI: 10.5517/ccdc.csd.cc23156k
title: CCDC 1937537: Experimental Crystal Structure Determination

description: Related Article: Ming-Gang Zhao, Li Li, Ru-Ru He, Xiang Zhang, Jian-Ping Ma, Ji-Hu Su, Wenjun Zheng|2020|Dalton Trans.|49|15190|doi:10.1039/D0DT03225E
DOI: 10.5517/ccdc.csd.cc23157l
title: CCDC 1937538: Experimental Crystal Structure Determination

description: Related Article: Ming-Gang Zhao, Li Li, Ru-Ru He, Xiang Zhang, Jian-Ping Ma, Ji-Hu Su, Wenjun Zheng|2020|Dalton Trans.|49|15190|doi:10.1039/D0DT03225E
DOI: 10.5517/ccdc.csd.cc23705k
title: CCDC 1943147: Experimental Crystal Structure Determination

description: Related Article: Dirk J. Schild, Marcus W. Drover, Paul H. Oyala, Jonas C. Peters|2020|J.Am.Chem.Soc.|142|18963|doi:10.1021/jacs.0c09363
DOI: 10.5517/ccdc.csd.cc23706l
title: CCDC 1943148: Experimental Crystal Structure Determination

description: Related Article: Dirk J. Schild, Marcus W. Drover, Paul H. Oyala, Jonas C. Peters|2020|J.Am.Chem.Soc.|142|18963|doi:10.1021/jacs.0c09363
DOI: 10.5517/ccdc.csd.cc255j41
title: CCDC 2001302: Experimental Crystal Structure Determination

description: Related Article: Hendrik Tinnermann, Craig Fraser, Rowan D. Young
|2020|Dalton Trans.|49|15184|doi:10.1039/D0DT03551C
DOI: 10.5517/ccdc.csd.cc255j52
title: CCDC 2001303: Experimental Crystal Structure Determination

description: Related Article: Hendrik Tinnermann, Craig Fraser, Rowan D. Young
|2020|Dalton Trans.|49|15184|doi:10.1039/D0DT03551C
DOI: 10.5517/ccdc.csd.cc255j63
title: CCDC 2001304: Experimental Crystal Structure Determination

description: Related Article: Hendrik Tinnermann, Craig Fraser, Rowan D. Young
|2020|Dalton Trans.|49|15184|doi:10.1039/D0DT03551C
DOI: 10.5517/ccdc.csd.cc25n9pv
title: CCDC 2015517: Experimental Crystal Structure Determination

description: Related Article: Tamara A. Bazhenova, Leokadiya V. Zorina, Sergey V. Simonov, Vladimir S. Mironov, Olga V. Maximova, Lena Spillecke, Changhyun Koo, Rüdiger Klingeler, Yuriy V. Manakin, Alexander N. Vasiliev, Eduard B. Yagubskii|2020|Dalton Trans.|49|15287|doi:10.1039/D0DT03092A
DOI: 10.5517/ccdc.csd.cc25n9qw
title: CCDC 2015518: Experimental Crystal Structure Determination

description: Related Article: Tamara A. Bazhenova, Leokadiya V. Zorina, Sergey V. Simonov, Vladimir S. Mironov, Olga V. Maximova, Lena Spillecke, Changhyun Koo, Rüdiger Klingeler, Yuriy V. Manakin, Alexander N. Vasiliev, Eduard B. Yagubskii|2020|Dalton Trans.|49|15287|doi:10.1039/D0DT03092A
DOI: 10.5517/ccdc.csd.cc25n9rx
title: CCDC 2015519: Experimental Crystal Structure Determination

description: Related Article: Tamara A. Bazhenova, Leokadiya V. Zorina, Sergey V. Simonov, Vladimir S. Mironov, Olga V. Maximova, Lena Spillecke, Changhyun Koo, Rüdiger Klingeler, Yuriy V. Manakin, Alexander N. Vasiliev, Eduard B. Yagubskii|2020|Dalton Trans.|49|15287|doi:10.1039/D0DT03092A
DOI: 10.5517/ccdc.csd.cc25v8nz
title: CCDC 2021251: Experimental Crystal Structure Determination

description: Related Article: Nagaraju Barsu, Markus Leutzsch, Alois Fürstner|2020|J.Am.Chem.Soc.|142|18746|doi:10.1021/jacs.0c08582
DOI: 10.5517/ccdc.csd.cc25v8p0
title: CCDC 2021252: Experimental Crystal Structure Determination

description: Related Article: Nagaraju Barsu, Markus Leutzsch, Alois Fürstner|2020|J.Am.Chem.Soc.|142|18746|doi:10.1021/jacs.0c08582
DOI: 10.5517/ccdc.csd.cc25v8q1
title: CCDC 2021253: Experimental Crystal Structure Determination

description: Related Article: Nagaraju Barsu, Markus Leutzsch, Alois Fürstner|2020|J.Am.Chem.Soc.|142|18746|doi:10.1021/jacs.0c08582
DOI: 10.5517/ccdc.csd.cc25v8r2
title: CCDC 2021254: Experimental Crystal Structure Determination

description: Related Article: Nagaraju Barsu, Markus Leutzsch, Alois Fürstner|2020|J.Am.Chem.Soc.|142|18746|doi:10.1021/jacs.0c08582
DOI: 10.5517/ccdc.csd.cc25v8s3
title: CCDC 2021255: Experimental Crystal Structure Determination

description: Related Article: Nagaraju Barsu, Markus Leutzsch, Alois Fürstner|2020|J.Am.Chem.Soc.|142|18746|doi:10.1021/jacs.0c08582
DOI: 10.5281/zenodo.4277920
title: MCMC Chains and Maximum Likelihood Parameters for a Random Walk Model of Dark Matter Halo Concentrations

description: MCMC chains and the maximum likelihood model parameter file associated with the random walk dark matter halo concentration model of Johnson, Benson, & Grin (2020; https://arxiv.org/abs/2006.15231). See the README file for details.
DOI: 10.5281/zenodo.4277919
title: MCMC Chains and Maximum Likelihood Parameters for a Random Walk Model of Dark Matter Halo Concentrations

description: MCMC chains and the maximum likelihood model parameter file associated with the random walk dark matter halo concentration model of Johnson, Benson, & Grin (2020; https://arxiv.org/abs/2006.15231). See the README file for details.
DOI: 10.6084/m9.figshare.13247066
title: Data for What’s Up with the Supp?: An Analysis of Supplementary Material in PubMed Central

description: This spreadsheet contains raw, cleaned, and curated data from the PMC Project 'What's Up with the Supp?: An Analysis of Supplementary Materials in PMC,' led by the 2019-2020 Associate Fellows at the National Library of Medicine. In addition to the data, visuals and tables are also included in subsequent spreadsheet tabs.
DOI: 10.6084/m9.figshare.13247066.v1
title: Data for What’s Up with the Supp?: An Analysis of Supplementary Material in PubMed Central

description: This spreadsheet contains raw, cleaned, and curated data from the PMC Project 'What's Up with the Supp?: An Analysis of Supplementary Materials in PMC,' led by the 2019-2020 Associate Fellows at the National Library of Medicine. In addition to the data, visuals and tables are also included in subsequent spreadsheet tabs.
DOI: 10.5281/zenodo.4277956
title: Long read proteogenomics codeathon

description: Datasets for the CSHL LRP Codeathon team
DOI: 10.5281/zenodo.4277941
title: WarpX Accelerated Nodes Parallel Computing Paper

description: This dataset contains the inputs, outputs, job submission scripts, and executables<br> used to create the Figures in "Porting WarpX to GPU-accelerated platforms" by A. Myers<br> et. al, submitted to Parallel Computing as part of the ECP Special Issue on Transitioning<br> to Accelerated nodes. These results were obtained using the October, 2020 release tags of WarpX and AMReX,<br> available on Github here: https://github.com/ECP-WarpX/WarpX and here: https://github.com/AMReX-Codes/amrex The following module files were loaded on Summit: 1) hsi/5.0.2.p5 2) xalt/1.2.0 3) lsf-tools/2.0 4) darshan-runtime/3.1.7<br> 5) DefApps 6) cuda/10.1.243 7) gcc/6.4.0 8) spectrum-mpi/10.3.1.2-20200121 To use nsight-compute for the roofline plots, we also loaded: nsight-compute/2020.1.2 Manifest: BinScan: contains material used to make Figure 1. To generate the figure, use the<br> Jupyter notebook called "bin_size.ipynb". StrongScaling: contains material used to make Figure 5. To generate the figure, use the<br> Jupyter notebook called "strong_scaling.ipynb". WeakScalingCPU: contains material used to make Figure 4. To generate the figure, use the<br> Jupyter notebook called "weak_scaling.ipynb". WeakScalingGPU: contains material used to make Figure 5. To generate the figure, use the<br> Jupyter notebook called "weak_scaling.ipynb". Roofline: contains material used to make the roofline plots (Figures 2 and 3). This<br> includes output generated using nsight-compute with WarpX and python scripts for<br> processing and plotting these output files. These scripts and methodology originally<br> come from Charlene Yang at NERSC. The file "script.sh" was used to generate the<br> profiler output
DOI: 10.5281/zenodo.4170603
title: WarpX Accelerated Nodes Parallel Computing Paper

description: This dataset contains the inputs, outputs, job submission scripts, and executables<br> used to create the Figures in "Porting WarpX to GPU-accelerated platforms" by A. Myers<br> et. al, submitted to Parallel Computing as part of the ECP Special Issue on Transitioning<br> to Accelerated nodes. These results were obtained using the October, 2020 release tags of WarpX and AMReX,<br> available on Github here: https://github.com/ECP-WarpX/WarpX and here: https://github.com/AMReX-Codes/amrex The following module files were loaded on Summit: 1) hsi/5.0.2.p5 2) xalt/1.2.0 3) lsf-tools/2.0 4) darshan-runtime/3.1.7<br> 5) DefApps 6) cuda/10.1.243 7) gcc/6.4.0 8) spectrum-mpi/10.3.1.2-20200121 To use nsight-compute for the roofline plots, we also loaded: nsight-compute/2020.1.2 Manifest: BinScan: contains material used to make Figure 1. To generate the figure, use the<br> Jupyter notebook called "bin_size.ipynb". StrongScaling: contains material used to make Figure 5. To generate the figure, use the<br> Jupyter notebook called "strong_scaling.ipynb". WeakScalingCPU: contains material used to make Figure 4. To generate the figure, use the<br> Jupyter notebook called "weak_scaling.ipynb". WeakScalingGPU: contains material used to make Figure 5. To generate the figure, use the<br> Jupyter notebook called "weak_scaling.ipynb". Roofline: contains material used to make the roofline plots (Figures 2 and 3). This<br> includes output generated using nsight-compute with WarpX and python scripts for<br> processing and plotting these output files. These scripts and methodology originally<br> come from Charlene Yang at NERSC. The file "script.sh" was used to generate the<br> profiler output
DOI: 10.5281/zenodo.4277891
title: OSeaIce post-processed data

description: These are the EC-Earth3 post-processed data that can be used with the associated Python scripts (https://doi.org/10.5281/zenodo.4277877) in order to produce the figures of the paper that summarizes the results arising from the sensitivity experiments carried out in the framework of the EU Horizon 2020 OSeaIce project (Marie Slodowska-Curie project, grant agreement no. 834493): Docquier et al. (2020). Impact of ocean heat transport on the Arctic sea-ice decline: A model study with EC-Earth3. Accepted in Climate Dynamics.
DOI: 10.6084/m9.figshare.13249619.v1
title: AJP_Supplementary_Tables.pdf

description: Comprehensive summary of aerobic, resistance, and swimming training in cardiometabolic disease mouse models.<br>
DOI: 10.6084/m9.figshare.13249619.v2
title: AJP_Supplementary_Tables.pdf

description: Comprehensive summary of aerobic, resistance, and swimming training in cardiometabolic disease mouse models.<br>
DOI: 10.5281/zenodo.4271545
title: Novel Coronavirus (COVID-19) Cases in The Netherlands

description: On 27 February 2020, the first case of COVID-19 disease was confirmed in The Netherlands by RIVM (National Institute for Public Health and the Environment). In the weeks after, thousands of people were diagnosed with the infectious disease. Data on COVID-19 case counts are important for research and applications on various topics like epidemiology and statistics. This dataset contains reported case counts derived from official sources like RIVM (National Institute for Public Health and the Environment), LCPS (National Coordination Center for Patient Distribution), and NICE (National Intensive Care Evaluation). Data from these sources are collected, standardized, and published in various formats on a daily basis. The README document in this repository provides an overview of the available datasets, their file location(s), and codebooks. Copies of the original data are stored in the folder named 'raw_data'. Scripts to process the raw data into standardized files can be found in the folder workflows.
DOI: 10.17632/h38dhp558x.1
title: Energy-Dispersive X-ray Spectroscopy and Atom-probe Tomography Data of Doped Ceria Exposed to Extreme Conditions

description: Samples of ~1 μm films of CeO2 doped with 2 wt% Mo, 1.5 wt% Ru, 0.75 wt% Pd, 0.5 wt% Re and 0.25 wt% Rh grown with pulsed laser deposition were irradiated with I2+ ions (610°C and 730°C, 1016 and 5×1016 I2+/cm2). For selected samples post-irradiation heat treatment was conducted (900°C, 1100°C). The specimens were sectioned with focussed ion beam milling and characterized in a transmission electron microscope with energy dispesive x-ray spectroscopy, and with atom-probe tomography. Energy dispersive x-ray spectroscopy was used to obtain elemental maps showing the distribution of dopants in the specimen after exposure. Some of these maps are discussed in detail in our companion article “Formation of multicomponent alloy particles in doped ceria under I2+ ion irradiation and thermal annealing” in the Journal of Nuclear Materials [REF]. Advanced computational analysis could be used to more accurately quantify local compositions. Data is provided for additional regions of interest and one additional irradiation condition. The doped Ceria film that was heat treated at 1100 °C delaminated from the substrate in most places. Samples were extracted from the underside of a delaminated piece and analyzed with atom-probe tomography. The resulting data show Ceria and a Mo-rich particle and demonstrate that this approach is feasable in principle to study local compositions in a sample exposed to such extreme conditions.
DOI: 10.6084/m9.figshare.9943058
title: A global database of marine bivalve growth rates and parameters derived from von bertalanffy growth curves

description: The growth rates of bivalves are a commonly measured physiological characteristic, and have regularly been reported through the parameters of the von Bertalanffy growth model. Here, we provide a comprehensive database of measured values of growth rates from the scientific literature (1930 - 2019). Our data represents 243 species of bivalve from 44 families and all biogeographic realms. We provide metadata relating to the source, mean bottom temperature, geographic locations, particulate organic carbon flux to seafloor, and calculated overall growth performance. Data from species reared artificially for aquaculture have been excluded from this database, so our data only represent natural <i>in situ</i> populations. <br>The dataset provides an opportunity to critically examine relationship between growth rates, temperature, and geography. This contributes to a globally coherent understanding of how physiological parameters change across wide biogeographic boundaries, and how they may respond to climate change.
DOI: 10.5281/zenodo.4278034
title: Long read proteogenomics codeathon

description: Datasets for the CSHL LRP Codeathon team
DOI: 10.5281/zenodo.4274946
title: Development and Growth Factor Control of Epithelial-Mesenchymal Transition in Fetal and Adult Alveolar Type II Cells

description: Raw data
DOI: 10.5281/zenodo.4274945
title: Development and Growth Factor Control of Epithelial-Mesenchymal Transition in Fetal and Adult Alveolar Type II Cells

description: Raw data
DOI: 10.5517/ccdc.csd.cc1z17bl
title: CCDC 1818439: Experimental Crystal Structure Determination

description: Related Article: Jakku Ranjith Kumar, Eda Rami Reddy, Nedaossadat Mirzadeh, T. Srinivasa Reddy, Anil Kumar Vardhaman, L. Giribabu, Balasubramanian Sridhar, Deep Pooja, Sistla Ramakrisna, Suresh Bhargava, Rajiv Trivedi|2020|Polyhedron|192|114829|doi:10.1016/j.poly.2020.114829
DOI: 10.5517/ccdc.csd.cc1z17cm
title: CCDC 1818440: Experimental Crystal Structure Determination

description: Related Article: Jakku Ranjith Kumar, Eda Rami Reddy, Nedaossadat Mirzadeh, T. Srinivasa Reddy, Anil Kumar Vardhaman, L. Giribabu, Balasubramanian Sridhar, Deep Pooja, Sistla Ramakrisna, Suresh Bhargava, Rajiv Trivedi|2020|Polyhedron|192|114829|doi:10.1016/j.poly.2020.114829
DOI: 10.21223/xe9j6q/hf0udh
title: 03_VITC112018_JUNIN_CHULEC.tab

description: :unav
DOI: 10.21223/xe9j6q/u6qvme
title: 03_VITC112018_JUNIN_LAVICT.tab

description: :unav
DOI: 10.21223/xe9j6q/n147ro
title: Data_dictionary_VitaminC.tab

description: :unav
DOI: 10.21223/xe9j6q
title: Dataset for: Potential new bio-fortified potato varieties high in Vitamin C to contribute to food and nutrition security in Junin, Peru.

description: A select group of 30 tetraploid biofortified clones from cycle I will be evaluated using a farmer participatory variety selection (PVS) in the highland of Junin during the campaign 2018-2019; yield components, micronutrients content (Fe Zn) and post-harvest traits will be determinated, with the main purpose to improve the food security and nutrition of families in situation of vulnerability through the identification of 3-5 potential new biofortified potatoes varieties with 25% of micronutrient concentration (Fe) above the local varieties. The Vitamin C was conducted in CIP-La Molina. It is quantified by the spectrophotometric method and expressed in mg/100 g, fresh weight (FW).
DOI: 10.21223/wlnbyv/ezdrsa
title: 02_MIN112018_JUNIN_CHULEC.tab

description: :unav
DOI: 10.21223/wlnbyv/w8fqe0
title: 02_MIN112018_JUNIN_LAVICT.tab

description: :unav
DOI: 10.21223/wlnbyv/vcv2ac
title: Data_dictionary_minerals.tab

description: :unav