cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3888549
title: Looking Farther in Parametric Scene Parsing with Ground and Aerial Imagery

description: Corresponding aerial imagery to Argoverse and KITTI
DOI: 10.17632/r9m3rbmhkf.1
title: Data for: Screening and druggability analysis of some plant metabolites against SARS-CoV-2: an integrative computational approach
DOI: 10.5281/zenodo.3451267
title: Data for the publication "Impact of Isolated Atmospheric Aging processes on the Cloud Condensation Nucleiactivation of Soot Particles"

description: The repository contains the data for the paper: Friebel, F., Lobo, P., Neubauer, D., Lohmann, U., Drossaart van Dusseldorp, S., Mühlhofer, E., and Mensah, A. A.: Impact of isolated atmospheric aging processes on the cloud condensation nuclei activation of soot particles, Atmos. Chem. Phys., 19, 15545–15567, https://doi.org/10.5194/acp-19-15545-2019, 2019. Note that the scripts to plot this data are to be found in the accompanying package (http://dx.doi.org/10.5281/zenodo.3452036)
DOI: 10.5281/zenodo.3451266
title: Data for the publication "Impact of Isolated Atmospheric Aging processes on the Cloud Condensation Nucleiactivation of Soot Particles"

description: The repository contains the data for the paper: Friebel, F., Lobo, P., Neubauer, D., Lohmann, U., Drossaart van Dusseldorp, S., Mühlhofer, E., and Mensah, A. A.: Impact of isolated atmospheric aging processes on the cloud condensation nuclei activation of soot particles, Atmos. Chem. Phys., 19, 15545–15567, https://doi.org/10.5194/acp-19-15545-2019, 2019. Note that the scripts to plot this data are to be found in the accompanying package (http://dx.doi.org/10.5281/zenodo.3452036)
DOI: 10.5281/zenodo.3451289
title: Data for the publication "Ozone concentration versus Temperature: Atmospheric aging of soot particles"

description: The repository contains the data for the paper: Friebel, F and Mensah, A. A. Ozone concentration versus Temperature: Atmospheric aging of soot particles, <em>Langmuir </em> <strong>2019</strong>, 35, 45, 14437–14450 https://doi.org/10.1021/acs.langmuir.9b02372
DOI: 10.5281/zenodo.3451288
title: Data for the publication "Ozone concentration versus Temperature: Atmospheric aging of soot particles"

description: The repository contains the data for the paper: Friebel, F and Mensah, A. A. Ozone concentration versus Temperature: Atmospheric aging of soot particles, <em>Langmuir </em> <strong>2019</strong>, 35, 45, 14437–14450 https://doi.org/10.1021/acs.langmuir.9b02372
DOI: 10.5281/zenodo.3888189
title: Looking Farther in Parametric Scene Parsing withGround and Aerial Imagery

description: Corresponding aerial imagery to Argoverse and KITTI
DOI: 10.5281/zenodo.3840593
title: Accompanied data files used in the paper "Analysis of chromatin organization and gene expression in T cells identifies functional genes for rheumatoid arthritis"

description: lists of source file used in the paper "Analysis of chromatin organization and gene expression in T cells identifies functional genes for rheumatoid arthritis" by Jing Yang, Amanda McGovern, Paul Martin, Kate Duffus, Xiangyu Ge, Peyman Zarrineh, Andrew P Morris, Antony Adamson, Peter Fraser, Magnus Rattray & Stephen Eyre. The paper has been accepted by Nature Communications.
description: raw sequencing data available on https://www.ncbi.nlm.nih.gov/gds/?term=GSE138767
DOI: 10.5281/zenodo.3888690
title: Watershed shapes for the Great Lakes Runoff Inter-comparison Project for Lake Erie (GRIP-E)

description: This dataset provides the shapefiles of the 46 calibration and 7 validation watersheds used within the Great Lakes Runoff Inter-comparison Project for Lake Erie (GRIP-E). The watersheds draining towards the following WSC and USGS gauge stations have been used for: <strong>1a. calibration of objective 1</strong> (low-human impact)<br> (28 stations in total; 13 of them in both objective 1 and 2)<br> 02GA010 02GA018 02GA038 02GA047 02GB007<br> 02GC002 02GC010 02GC018 02GD004 02GE007<br> 02GG002 02GG003 02GG006 02GG009 02GG013<br> 04159492 04159900 04160600 04161820 04164000<br> 04165500 04166100 04177000 04196800 04197100<br> 04207200 04208504 04213000 <strong>1b. calibration of objective 2</strong> (most-downstream gauges closest to Lake Erie)<br> (31 stations in total; 13 of them in both objective 1 and 2)<br> 02GB001 02GB007 02GC002 02GC007 02GC018<br> 02GC026 02GE007 02GG003 02GG009 02GG013<br> 04159900 04160600 04165500 04166500 04174500<br> 04176500 04177000 04193500 04195820 04198000<br> 04199000 04199500 04200500 04208504 04209000<br> 04212100 04213000 04213500 04214500 04215000<br> 04215500 <strong>2. (spatial) validation</strong><br> (7 stations in total)<br> 02GE003 04167000 04168000 04185000 04195500<br> 04201500 04208000 =============================================================== These data have been derived under the Great Lakes Runoff Intercomparison Project for Lake Erie (GRIP-E) led by Juliane Mai and Bryan Tolson (both University of Waterloo) and funded under the Integrated Modelling Program for Canada (IMPC) within the Global Water Futures program.
DOI: 10.5281/zenodo.3888689
title: Watershed shapes for the Great Lakes Runoff Inter-comparison Project for Lake Erie (GRIP-E)

description: This dataset provides the shapefiles of the 46 calibration and 7 validation watersheds used within the Great Lakes Runoff Inter-comparison Project for Lake Erie (GRIP-E). The watersheds draining towards the following WSC and USGS gauge stations have been used for: <strong>1a. calibration of objective 1</strong> (low-human impact)<br> (28 stations in total; 13 of them in both objective 1 and 2)<br> 02GA010 02GA018 02GA038 02GA047 02GB007<br> 02GC002 02GC010 02GC018 02GD004 02GE007<br> 02GG002 02GG003 02GG006 02GG009 02GG013<br> 04159492 04159900 04160600 04161820 04164000<br> 04165500 04166100 04177000 04196800 04197100<br> 04207200 04208504 04213000 <strong>1b. calibration of objective 2</strong> (most-downstream gauges closest to Lake Erie)<br> (31 stations in total; 13 of them in both objective 1 and 2)<br> 02GB001 02GB007 02GC002 02GC007 02GC018<br> 02GC026 02GE007 02GG003 02GG009 02GG013<br> 04159900 04160600 04165500 04166500 04174500<br> 04176500 04177000 04193500 04195820 04198000<br> 04199000 04199500 04200500 04208504 04209000<br> 04212100 04213000 04213500 04214500 04215000<br> 04215500 <strong>2. (spatial) validation</strong><br> (7 stations in total)<br> 02GE003 04167000 04168000 04185000 04195500<br> 04201500 04208000 =============================================================== These data have been derived under the Great Lakes Runoff Intercomparison Project for Lake Erie (GRIP-E) led by Juliane Mai and Bryan Tolson (both University of Waterloo) and funded under the Integrated Modelling Program for Canada (IMPC) within the Global Water Futures program.
DOI: 10.25934/00005901
title: sGC change in COVID-19 patients lung

description: Vivli is an independent, non-profit organization that has developed a global data-sharing and analytics platform to serve all elements of the international research community. Our mission is to promote, coordinate, and facilitate scientific sharing and reuse of clinical research data through the creation and implementation of a sustainable global data-sharing enterprise. The Vivli platform includes an independent data repository, in-depth search engine and a cloud-based, secure analytics platform.
DOI: 10.5281/zenodo.3888658
title: Gadgetron 2020 Summer School Raw Data Set

description: These are the data that will be used for the live coding/demonstration sessions in the Gadgetron 2020 Summer Course sessions. The layout of files in the unpacked archive should reflect the sessions / lectures on each day.
DOI: 10.5281/zenodo.3888705
title: Ionospheric Vertical Correlation Length Derived From IRI-2016 Model Errors

description: Ionospheric vertical correlation length based on IRI-2016 model and Incoherent Scatter Radar (ISR) data. ISR used for this analysis: Jicamarca, Arecibo, Millstone Hill, Poker Flat ISR, and ResoluteBay Canada ISR. This metadata can be used for the construction of the covariance matrix for ionospheric data assimilation. Inside of the .nc file: lat=array of geomagnetic latitudes (degrees)<br> alt=arrays of altitudes (km)<br> vert_corr1=array of size (nalt, nlat), contains vertical correlation length above the reference point for different latitudes<br> vert_corr2=array of size (nalt, nlat), contains vertical correlation length below the reference point for different latitudes<br>
DOI: 10.5281/zenodo.3888714
title: Deep statistical solver : linear systems

description: Dataset of linear systems for the training and testing of a deep statistical solver
DOI: 10.5281/zenodo.3888523
title: 25-year history of volcano magma supply in the east-central Aleutian arc, Alaska: Dataset

description: Dataset used in the paper "25-year history of volcano magma supply in the east-central Aleutian arc, Alaska".
DOI: 10.5281/zenodo.3888522
title: 25-year history of volcano magma supply in the east-central Aleutian arc, Alaska: Dataset

description: Dataset used in the paper "25-year history of volcano magma supply in the east-central Aleutian arc, Alaska".
DOI: 10.21227/4sn5-0s18
title: COVID-19 tweets dataset for Bengali language

description: This dataset consists of tweets related to COVID-19 that have been crawled from twitter. It consists of 36117 unique tweets. It consists of 34 fields though NULL values are also present for some fields in some samples. This dataset contains all the text data in Bengali language and there is no further need to make use of any tools or twitter api to download the textual data. The keywords that were used to crawl the tweets are:1) corona 2) covid19 3) coronavirus 4) covid 5) sarscov2 The various fields present in the dataset are:1) tweet2) id3) username4) place5) near6) user_rt_id7) video8) name9) trans_src10) likes_count11) retweet_id12) urls13) link14) translate15) photos16) user_id17) mentions18) reply_to19) source20) time21) user_rt22) geo23) timezone24) retweets_count25) cashtags26) trans_dest27) retweet_date28) quote_url29) conversation_id30) created_at31) retweet32) hashtags33) date34) replies_count
DOI: 10.5281/zenodo.3860527
title: Thermodynamics data of Alkali Feldspars from FPMD simulations

description: We computed the thermodynamic properties (pressure, temperature, internal energy, heat capacity) and thermoelastic coefficients (isobaric expansivity, isothermal compressibility, thermal pressure coefficient) on the two alkali feldspars end-members using <em>ab initio</em> molecular dynamics simulation in the 2000-20000 K temperature range and in the 0.5-6 g.cm<sup>-3</sup> density range. Simulations are performed using the Vienna Ab Initio Simulation Package (VASP) (Kresse and Furthmuller, 1996) in the canonical (NVT) ensemble with a timestep of 0.5-2 fs for 5-20 ps depending on the temperature and density. We model the feldspar end-members in a cubic cell containing 208 atoms (16 formula units) and 1024 or 1152 electrons for the Na- and K-feldspars respectively. For simulations at low density we used pseudopotentials which require a lower plane wave energy cutoff, set to 370 eV. For Na-end-member, we also used hard pseudopotentials at high density in order to reduce the overlap of electronic spheres, in particular for Na-Na pairs. The energy cutoff for this set of pseudopotentials is 950 eV. Additional details can be found in the manuscript. There is one dataset for each different composition and set of pseudopotentials used: kobsch-ds01.txt --&gt; NaAlSi<sub>3</sub>O<sub>8</sub> kobsch-ds02.txt --&gt; NaAlSi<sub>3</sub>O<sub>8 </sub>and set of pseudopotentials with a lower plane wave energy cutoff than in 01 kobsch-ds03.txt --&gt; NaAlSi<sub>3</sub>O<sub>8</sub> and harder pseudopotentials than in 01 kobsch-ds04.txt --&gt; KAlSi<sub>3</sub>O<sub>8</sub> kobsch-ds05.txt --&gt; KAlSi<sub>3</sub>O<sub>8</sub> and set of pseudopotentials with a lower plane wave energy cutoff than in 04 Each file present the arithmetic time averages of the pressure (P), temperature (T) and internal energy (E). The standard deviation of the data to the mean is indicated by stdev_X, where X is P, T or E. The statistical error to the mean (err_X) is computed using the blocking method as described by Flyvbjerg and Petersen (1989). The sign '&gt;' is indicated before the value of the statistical error when no convergence was reached during the estimation of this error. The heat capacity Cv is computed using fluctuations on both potential and kinetic energies (Allen and Tildesley, 1989) and its statistical error stdev_Cv is computed using the bootstrap method. We computed the thermoelastic coefficients only for densities (\(\rho\)) above 1.5 g.cm<sup>-3</sup>. The thermal pressure coefficient (TPC = \(\frac{\partial P}{\partial T}\big|_V\)) is the slope of linear fit of P vs. T isochores. The isothermal compressibility (\(\beta = -\frac{1}{\rho} \frac{\partial \rho}{\partial P}\big|_T\)) is computed using central finite differences on our P vs. \(\rho\) isotherms. The isobaric expansivity (\(\alpha = \frac{1}{\rho} \frac{\partial \rho}{\partial T}\big|_P\)) is computed using the previously computed \(\beta\) and TPC.
DOI: 10.5281/zenodo.3609985
title: Thermodynamics data of Alkali Feldspars from FPMD simulations

description: We computed the thermodynamic properties (pressure, temperature, internal energy, heat capacity) and thermoelastic coefficients (isobaric expansivity, isothermal compressibility, thermal pressure coefficient) on the two alkali feldspars end-members using <em>ab initio</em> molecular dynamics simulation in the 2000-20000 K temperature range and in the 0.5-6 g.cm<sup>-3</sup> density range. Simulations are performed using the Vienna Ab Initio Simulation Package (VASP) (Kresse and Furthmuller, 1996) in the canonical (NVT) ensemble with a timestep of 0.5-2 fs for 5-20 ps depending on the temperature and density. We model the feldspar end-members in a cubic cell containing 208 atoms (16 formula units) and 1024 or 1152 electrons for the Na- and K-feldspars respectively. For simulations at low density we used pseudopotentials which require a lower plane wave energy cutoff, set to 370 eV. For Na-end-member, we also used hard pseudopotentials at high density in order to reduce the overlap of electronic spheres, in particular for Na-Na pairs. The energy cutoff for this set of pseudopotentials is 950 eV. Additional details can be found in the manuscript. There is one dataset for each different composition and set of pseudopotentials used: kobsch-ds01.txt --&gt; NaAlSi<sub>3</sub>O<sub>8</sub> kobsch-ds02.txt --&gt; NaAlSi<sub>3</sub>O<sub>8 </sub>and set of pseudopotentials with a lower plane wave energy cutoff than in 01 kobsch-ds03.txt --&gt; NaAlSi<sub>3</sub>O<sub>8</sub> and harder pseudopotentials than in 01 kobsch-ds04.txt --&gt; KAlSi<sub>3</sub>O<sub>8</sub> kobsch-ds05.txt --&gt; KAlSi<sub>3</sub>O<sub>8</sub> and set of pseudopotentials with a lower plane wave energy cutoff than in 04 Each file present the arithmetic time averages of the pressure (P), temperature (T) and internal energy (E). The standard deviation of the data to the mean is indicated by stdev_X, where X is P, T or E. The statistical error to the mean (err_X) is computed using the blocking method as described by Flyvbjerg and Petersen (1989). The sign '&gt;' is indicated before the value of the statistical error when no convergence was reached during the estimation of this error. The heat capacity Cv is computed using fluctuations on both potential and kinetic energies (Allen and Tildesley, 1989) and its statistical error stdev_Cv is computed using the bootstrap method. We computed the thermoelastic coefficients only for densities (\(\rho\)) above 1.5 g.cm<sup>-3</sup>. The thermal pressure coefficient (TPC = \(\frac{\partial P}{\partial T}\big|_V\)) is the slope of linear fit of P vs. T isochores. The isothermal compressibility (\(\beta = -\frac{1}{\rho} \frac{\partial \rho}{\partial P}\big|_T\)) is computed using central finite differences on our P vs. \(\rho\) isotherms. The isobaric expansivity (\(\alpha = \frac{1}{\rho} \frac{\partial \rho}{\partial T}\big|_P\)) is computed using the previously computed \(\beta\) and TPC.
DOI: 10.21227/4kxx-9k92
title: COVID-19 tweets dataset for Spanish language

description: This dataset consists of tweets related to COVID-19 that have been crawled from twitter. It consists of 18958 unique tweets. It consists of 34 fields though NULL values are also present for some fields in some samples. This dataset contains all the text data in Spanish language and there is no further need to make use of any tools or twitter api to download the textual data.The keywords that were used to crawl the tweets are:1)corona 2)covid19 3) coronavirus 4) covid 5) sarscov2 The various fields present in the dataset are:1) tweet2) id3) username4) place5) near6) user_rt_id7) video8) name9) trans_src10) likes_count11) retweet_id12) urls13) link14) translate15) photos16) user_id17) mentions18)reply_to19)source20) time21) user_rt22) geo23) timezone24) retweets_count25) cashtags26) trans_dest27) retweet_date28) quote_url29) conversation_id30) created_at31) retweet32) hashtags33) date34) replies_count
DOI: 10.5281/zenodo.3888900
title: IO Islamic 2029. Ta'rîkh-i-Mûsawî, A History of the Prophet Moses

description: IO Islamic 2029. Ta’rîkh-i-Mûsawî, A History of the Prophet Moses
DOI: 10.5281/zenodo.3888899
title: IO Islamic 2029. Ta'rîkh-i-Mûsawî, A History of the Prophet Moses

description: IO Islamic 2029. Ta’rîkh-i-Mûsawî, A History of the Prophet Moses
DOI: 10.5281/zenodo.3888902
title: IO Islamic 2421. Nigâristân, Anecdotes and Curious Narratives of Celebrated Men from the Time of Nizâr bin Ma'add bin 'Adnân

description: IO Islamic 2421. Nigâristân, Anecdotes and Curious Narratives of Celebrated Men from the Time of Nizâr bin Ma’add bin ‘Adnân
DOI: 10.5281/zenodo.3888901
title: IO Islamic 2421. Nigâristân, Anecdotes and Curious Narratives of Celebrated Men from the Time of Nizâr bin Ma'add bin 'Adnân

description: IO Islamic 2421. Nigâristân, Anecdotes and Curious Narratives of Celebrated Men from the Time of Nizâr bin Ma’add bin ‘Adnân
DOI: 10.7910/dvn/c9thfq/8krlbw
title: Soil database.tab

description: :unav
DOI: 10.5281/zenodo.3888876
title: EUREC4A meso-scale cloud classifications

description: This dataset contains the manual cloud classifications of the EUREC4A field campaign time period as well as the post-processed data. The dataset is organised as following: <strong>zooniverse_raw:</strong> Originial output data from the platform zooniverse.org which has been used to create the classifications <strong>processed_data:</strong> <strong>Level1: EUREC4A_ManualClassifications_l1.nc:</strong> Geographical and Cartesian coordinates are added to each label <strong>Level2: EUREC4A_ManualClassifications_MergedClassifications.zarr.zip</strong>: labels are converted to masks and combined for each classification <strong>Level3: EUREC4A_ManualClassifications_l3_$WORKFLOW.zarr.zip</strong>: daily pixel-agreement among users/classifiers on each of the four meso-scale cloud patterns An example how to use the data can be found at github.com/observingClouds/EUREC4A_manualclassifications.
DOI: 10.5281/zenodo.3884682
title: Experimental Results of "Bioprinting Cell- and Spheroid-Laden Protein-Engineered Hydrogels as Tissue-on-Chip Platforms"

description: This repository contains the experimental results of the article "Bioprinting Cell- and Spheroid-Laden Protein-Engineered Hydrogels as Tissue-on-Chip Platforms" by Duarte Campos, D., Lindsay, C., Roth, J., LeSavage, B., Seymour, A., Krajina, B., Ribeiro, R., Costa, P., Heilshorn, S., published in <em>Front. bioeng. biotechnol. </em><strong>8, 374</strong> (2020). https://doi.org/10.3389/fbioe.2020.00374
description: This research was supported by the German Research Foundation (DFG-425869082 to DD) and the U.S. National Science Foundation (DMR-1808415 to SH) and the U.S. National Institutes of Health (R01-HL142718, R01-EB027171, R01-EB027666, R21-HL138042, R21-NS114549 to SH). This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 798014 (to PC). This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 828835 (to RR).
DOI: 10.5281/zenodo.3884681
title: Experimental Results of "Bioprinting Cell- and Spheroid-Laden Protein-Engineered Hydrogels as Tissue-on-Chip Platforms"

description: This repository contains the experimental results of the article "Bioprinting Cell- and Spheroid-Laden Protein-Engineered Hydrogels as Tissue-on-Chip Platforms" by Duarte Campos, D., Lindsay, C., Roth, J., LeSavage, B., Seymour, A., Krajina, B., Ribeiro, R., Costa, P., Heilshorn, S., published in <em>Front. bioeng. biotechnol. </em><strong>8, 374</strong> (2020). https://doi.org/10.3389/fbioe.2020.00374
description: This research was supported by the German Research Foundation (DFG-425869082 to DD) and the U.S. National Science Foundation (DMR-1808415 to SH) and the U.S. National Institutes of Health (R01-HL142718, R01-EB027171, R01-EB027666, R21-HL138042, R21-NS114549 to SH). This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 798014 (to PC). This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 828835 (to RR).
DOI: 10.5281/zenodo.3888964
title: somalier files for glioma dataset

description: somalier files for the glioma dataset used in the paper: run with: <pre><code class="language-bash">somalier relate -g glioma-somalier/groups.txt glioma-somalier/*.somalier </code></pre>
DOI: 10.5281/zenodo.3888963
title: somalier files for glioma dataset

description: somalier files for the glioma dataset used in the paper: run with: <pre><code class="language-bash">somalier relate -g glioma-somalier/groups.txt glioma-somalier/*.somalier </code></pre>
DOI: 10.7910/dvn/or5yhq/cr8h1t
title: newdata.tab

description: Main database
DOI: 10.7910/dvn/or5yhq/0m5mq0
title: Optimal Matching.R

description: Code for optimal matching for Figure A.1
DOI: 10.7910/dvn/or5yhq/whxsf9
title: matcheddata.tab

description: Subset of the data after performing optimal matching for Figure A.1
DOI: 10.5281/zenodo.1482091
title: Revealing Ferroelectric Switching Character Using Deep Recurrent Neural Networks

description: <strong>The ability to manipulate domains and domain walls underpins function in a range of next-generation applications of ferroelectrics. While there have been demonstrations of controlled nanoscale manipulation of domain structures to drive emergent properties, such approaches lack an internal feedback loop required for automation. Here, using a deep sequence-to-sequence autoencoder we automate the extraction of features of nanoscale ferroelectric switching from multichannel hyperspectral band-excitation piezoresponse force microscopy of tensile-strained PbZr<sub>0.2</sub>Ti<sub>0.8</sub>O<sub>3</sub> with a hierarchical domain structure. Using this approach, we identify characteristic behavior in the piezoresponse and cantilever resonance hysteresis loops, which allows for the classification and quantification of nanoscale-switching mechanisms. Specifically, we are able to identify elastic hardening events which are associated with the nucleation and growth of charged domain walls. This work demonstrates the efficacy of unsupervised neural networks in <em>learning</em> features of the physical response of a material from nanoscale multichannel hyperspectral imagery and provides new capabilities in leveraging multimodal <em>in operando</em> spectroscopies and automated control for the manipulation of nanoscale structures in materials.</strong>
description: Data for paper Revealing Ferroelectric Switching Character Using Deep Recurrent Neural Networks