cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4604568
title: Single-Cell Gene Expression Profiles for Classification Problems

description: This repository contains a collection of three datasets we use to introduce the Gene Mover Distance in [1] and described below. The three datasets are exported with a basic text-based format (.csv file) like other public datasets largely used in the Machine Learning community. The three datasets are extracted from the Gene Expression Omnibus (GEO) database [2], where they appear, respectively, with access number GSE116256 (blood leukemia, [3]), GSE84133 (human pancreas, [4]), and GSE67835 (human brain, [5]). In GEO, the datasets are decomposed into several files, which contain much more details than those reported in this version. However, the proposed format should facilitate other researchers in using this data. The Gene Mover's Distance is a measure of similarity between a pair of cells based on their gene expression profiles obtained via single-cell RNA sequencing. The underlying idea of GMD is to interpret the gene expression array of a single cell as a discrete probability measure. The distance between two cells is hence computed by solving an Optimal Transport problem between the two corresponding discrete measures. The Gene Mover's Distance can be used, for instance, to solve two classification problems: the classification of cells according to their condition and according to their type. The repository contains a python script to check the basic statistics of the data. [1] Bellazzi, R., Codegoni, A., Gualandi, S., Nicora, G., Vercesi, E. <em>The Gene Mover's Distance: Single-cell similarity via Optimal Transport</em>. https://arxiv.org/abs/2102.01218 [2] Gene Expression Omnibus (GEO) database, http://www.ncbi.nlm.nih.gov/geo [3] van Galen, P., Hovestadt, V., Wadsworth II, M.H., Hughes, T.K., Griffin, G.K., Battaglia, S., Verga, J.A., Stephansky, J., Pastika, T.J., Story, J.L. and Pinkus, G.S., 2019. <em>Single-cell RNA-seq reveals AML hierarchies relevant to disease progression and immunity</em>. Cell, 176(6), pp.1265-1281. [4] Baron, M., Veres, A., Wolock, S.L., Faust, A.L., Gaujoux, R., Vetere, A., Ryu, J.H., Wagner, B.K., Shen-Orr, S.S., Klein, A.M. and Melton, D.A., 2016.<em> A single-cell transcriptomic map of the human and mouse pancreas reveals inter-and intra-cell population structure</em>. Cell systems, 3(4), pp.346-360. [5] Darmanis, S., Sloan, S.A., Zhang, Y., Enge, M., Caneda, C., Shuer, L.M., Gephart, M.G.H., Barres, B.A. and Quake, S.R., 2015. <em>A survey of human brain transcriptome diversity at the single cell level</em>. Proceedings of the National Academy of Sciences, 112(23), pp.7285-7290.
DOI: 10.22008/fk2/kwazgt/dtt49u
title: ALS_readme-1.txt

description: laser altimetry readme
DOI: 10.22008/fk2/kwazgt/jvqdqm
title: ALS_2007.ave

description: 2007 laser altimetry data
DOI: 10.22008/fk2/kwazgt/qxsysr
title: ALS_2011.ave

description: 2011 laser altimetry data
DOI: 10.22008/fk2/kwazgt/gxvirq
title: ARS_2011.ave

description: 2011 radar altimetry data
DOI: 10.22008/fk2/kwazgt/cb8hcv
title: ARS_readme-1.txt

description: radar altimetry readme
DOI: 10.22008/fk2/kwazgt/xfqlch
title: ALS_2015.ave

description: 2015 laser altimetry data
DOI: 10.22008/fk2/kwazgt/6djwcb
title: ARS_2007.ave

description: 2007 radar altimetry data
DOI: 10.21942/uva.14216504
title: Manual Delineation approaches for direct imaging of the subcortex

description: The growing interest in the human subcortex is accompanied by an increasing number of parcellation procedures to identify deep brain structures in magnetic resonance imaging (MRI) contrasts. Manual procedures continue to form the gold standard for parcellating brain structures and is used for the validation of automated approaches. Performing manual parcellations is a tedious process which requires a systematic and reproducible approach. For this purpose, we created a series of anatomical protocols for the delineation of 21 individual subcortical structures. These protocols are augmented with three example MRI datasets combined with their manual delineations. The intelligibility of the protocols was assessed by calculating Dice similarity coefficients which showed that manual parcellations created using these protocols can provide high quality training data for automated algorithms. The protocols can be applied to create high quality training data for automated parcellation procedures, as well as for further validation of existing procedures and are shared without restrictions with the research community.
DOI: 10.21942/uva.14216504.v2
title: Manual Delineation approaches for direct imaging of the subcortex

description: The growing interest in the human subcortex is accompanied by an increasing number of parcellation procedures to identify deep brain structures in magnetic resonance imaging (MRI) contrasts. Manual procedures continue to form the gold standard for parcellating brain structures and is used for the validation of automated approaches. Performing manual parcellations is a tedious process which requires a systematic and reproducible approach. For this purpose, we created a series of anatomical protocols for the delineation of 21 individual subcortical structures. These protocols are augmented with three example MRI datasets combined with their manual delineations. The intelligibility of the protocols was assessed by calculating Dice similarity coefficients which showed that manual parcellations created using these protocols can provide high quality training data for automated algorithms. The protocols can be applied to create high quality training data for automated parcellation procedures, as well as for further validation of existing procedures and are shared without restrictions with the research community.
DOI: 10.21942/uva.14216504.v1
title: Manual Delineation approaches for direct imaging of the subcortex

description: The growing interest in the human subcortex is accompanied by an increasing number of parcellation procedures to identify deep brain structures in magnetic resonance imaging (MRI) contrasts. Manual procedures continue to form the gold standard for parcellating brain structures and is used for the validation of automated approaches. Performing manual parcellations is a tedious process which requires a systematic and reproducible approach. For this purpose, we created a series of anatomical protocols for the delineation of 21 individual subcortical structures. These protocols are augmented with three example MRI datasets combined with their manual delineations. The intelligibility of the protocols was assessed by calculating Dice similarity coefficients which showed that manual parcellations created using these protocols can provide high quality training data for automated algorithms. The protocols can be applied to create high quality training data for automated parcellation procedures, as well as for further validation of existing procedures and are shared without restrictions with the research community.
DOI: 10.21227/qyrt-tf59
title: regress

description: regress
DOI: 10.5281/zenodo.4606729
title: Supplementary Materials for "Dualism and superposition in the analysis of English synthetic compounds ending in '-er'", published in Linguistics

description: This folder contains the Supplementary Materials for the article on "Dualism and superposition in the analysis of English synthetic compounds ending in '-er'", published in Linguistics
DOI: 10.5281/zenodo.4606728
title: Supplementary Materials for "Dualism and superposition in the analysis of English synthetic compounds ending in '-er'", published in Linguistics

description: This folder contains the Supplementary Materials for the article on "Dualism and superposition in the analysis of English synthetic compounds ending in '-er'", published in Linguistics
DOI: 10.5281/zenodo.4606734
title: A Biomedically oriented automatically annotated Twitter COVID-19 Dataset

description: <strong>The use of social media data, like Twitter, for biomedical research has been gradually increasing over the years. With the COVID-19 pandemic, researchers have turned to more non-traditional sources of clinical data to characterize the disease in near-real time, study the societal implications of interventions, as well as the sequelae that recovered COVID-19 cases present (Long-). However, manually curated social media datasets are difficult to come by due to the expensive costs of manual annotation and the efforts needed to identify the correct texts. When datasets are available, they are usually very small and their annotations don’t generalize well over time or to larger sets of documents. As part of the 2021 Biomedical Linked Annotation Hackathon, we release our dataset of over 120 million automatically annotated tweets for biomedical research purposes. Incorporating best-practices, we identify tweets with potentially high clinical relevance. We evaluated our work by comparing several SpaCy-based annotation frameworks against a manually annotated gold-standard dataset. Selecting the best method to use for automatic annotation, we then annotated 120 million tweets and released them publicly for future downstream usage within the biomedical domain.</strong>
description: Usage details for this repository - https://github.com/thepanacealab/annotated_twitter_covid19_dataset And the paper available at: NOTE: The full text of the tweet cannot be shared, but each individual tweet can be hydrated back to get all data elements.
DOI: 10.5281/zenodo.4606733
title: A Biomedically oriented automatically annotated Twitter COVID-19 Dataset

description: <strong>The use of social media data, like Twitter, for biomedical research has been gradually increasing over the years. With the COVID-19 pandemic, researchers have turned to more non-traditional sources of clinical data to characterize the disease in near-real time, study the societal implications of interventions, as well as the sequelae that recovered COVID-19 cases present (Long-). However, manually curated social media datasets are difficult to come by due to the expensive costs of manual annotation and the efforts needed to identify the correct texts. When datasets are available, they are usually very small and their annotations don’t generalize well over time or to larger sets of documents. As part of the 2021 Biomedical Linked Annotation Hackathon, we release our dataset of over 120 million automatically annotated tweets for biomedical research purposes. Incorporating best-practices, we identify tweets with potentially high clinical relevance. We evaluated our work by comparing several SpaCy-based annotation frameworks against a manually annotated gold-standard dataset. Selecting the best method to use for automatic annotation, we then annotated 120 million tweets and released them publicly for future downstream usage within the biomedical domain.</strong>
description: Usage details for this repository - https://github.com/thepanacealab/annotated_twitter_covid19_dataset And the paper available at: NOTE: The full text of the tweet cannot be shared, but each individual tweet can be hydrated back to get all data elements.
DOI: 10.5281/zenodo.4607196
title: RCMI-dataset

description: Tecplot files of the RCM-I simulations.
description: Contact: sadeghzadeh@sustech.edu.cn
DOI: 10.5281/zenodo.4431029
title: RCMI-dataset

description: Tecplot files of the RCM-I simulations.
description: Contact: sadeghzadeh@sustech.edu.cn
DOI: 10.6084/m9.figshare.14222123
title: Potentilla_anserina.transcripts.fa

description: A total of 47,505 transcripts of <i>Potentilla anserina</i> genome
DOI: 10.6084/m9.figshare.14222201
title: Supplementary Material for: Soluble ST2 and Galectin-3 as Predictors of Chronic Kidney Disease Progression and Outcomes

description: <b><i>Background:</i></b> Soluble suppression of tumorigenicity-2 (sST2) and galectin-3, novel biomarkers of heart failure and cardiovascular stress, predict cardiovascular events (CVEs) and mortality. However, their relationship with kidney function and adverse outcomes in CKD are uncertain. The purpose of this study was to determine the association between sST2 and galectin-3 with CKD progression and adverse clinical outcomes. <b><i>Methods:</i></b> We measured baseline sST2 and galectin-3 levels in the CKD patient cohort at our institution between October 2013 and December 2014. The primary outcome was CKD progression (kidney failure with replacement therapy or ≥50% reduction in estimated glomerular filtration rate from the baseline). The secondary outcome was the composite of CVEs and death. We used a Cox proportional hazards model to evaluate the associations between sST2 and galectin-3 levels, with kidney and clinical outcomes. <b><i>Results:</i></b> In total, 352 patients were enrolled in this study. At baseline, log sST2 and galectin-3 were directly associated with the serum creatinine (Cr) and urine protein-to-Cr ratio. Cox regression analysis showed that the baseline log sST2 level independently predicted CKD progression and composite outcome after adjustment for age, sex, smoking, diabetes mellitus, hypertension, cardiovascular disease, renin-angiotensin system blocker, calcium channel blocker, β-blocker, diuretics, antiplatelet agents, anemia, and hypoalbuminemia. The baseline log galectin-3 level was independently associated with CKD progression, but not with the composite outcome after adjustment for confounding variables. <b><i>Conclusions:</i></b> Elevated levels of sST2 and galectin-3 are significantly associated with CKD progression, but only sST2 is associated with adverse clinical outcomes.
DOI: 10.3886/e112860v1-81400
title: Replication data for: Trade and the Global Recession

description: We develop a dynamic multicountry general equilibrium model to investigate forces acting on the global economy during the Great Recession and ensuing recovery. Our multisector framework accounts completely for countries' trade, investment, production, and GDPs in terms of different sets of shocks. Applying the model to 21 countries, we investigate the 29 percent drop in world trade in manufactures during the period 2008-2009. A shift in final spending away from tradable sectors, largely caused by declines in durables investment efficiency, accounts for most of the collapse in trade relative to GDP. Shocks to trade frictions, productivity, and demand play minor roles.
DOI: 10.6084/m9.figshare.12579152.v1
title: Potentilla_anserina.gff3

description: Gene structure annotation file
DOI: 10.5281/zenodo.4607210
title: Regulation_bacterial_population/Datasets

description: All datasets used to estimate <em>w</em>MelPop relative density and average Octomom copy number and to perform data visualizations and statistical analyzes. ### Exp #1<br> Names in the datasets | Names in the manuscript<br> ------------------------------ | --------------------------------<br> AR | Arabia-MP1<br> BR | RC-MP1<br> BO | Bolivia-MP1<br> CA | China-MP1<br> SE | USA-MP1<br> SF | France-MP1<br> WM | W1118-MP<br> CS | W1118-CS ### Exp #2<br> Names in the datasets | Names in the manuscript<br> ------------------------------ | --------------------------------<br> BO | Bolivia-MP1<br> SE | USA-MP1<br> RBO | Bolivia-MP2<br> RSE | USA-MP2<br> WM | W1118-MP ### Exp #3<br> Names in the datasets | Names in the manuscript<br> ------------------------------ | ----------------------<br> BO | Bolivia-MP1<br> SE | USA-MP1<br> SEBO | Bolivia(USA-MP1)<br> BOSE | USA(Bolivia-MP1)
DOI: 10.5281/zenodo.4607209
title: Regulation_bacterial_population/Datasets

description: All datasets used to estimate <em>w</em>MelPop relative density and average Octomom copy number and to perform data visualizations and statistical analyzes. ### Exp #1<br> Names in the datasets | Names in the manuscript<br> ------------------------------ | --------------------------------<br> AR | Arabia-MP1<br> BR | RC-MP1<br> BO | Bolivia-MP1<br> CA | China-MP1<br> SE | USA-MP1<br> SF | France-MP1<br> WM | W1118-MP<br> CS | W1118-CS ### Exp #2<br> Names in the datasets | Names in the manuscript<br> ------------------------------ | --------------------------------<br> BO | Bolivia-MP1<br> SE | USA-MP1<br> RBO | Bolivia-MP2<br> RSE | USA-MP2<br> WM | W1118-MP ### Exp #3<br> Names in the datasets | Names in the manuscript<br> ------------------------------ | ----------------------<br> BO | Bolivia-MP1<br> SE | USA-MP1<br> SEBO | Bolivia(USA-MP1)<br> BOSE | USA(Bolivia-MP1)
DOI: 10.5281/zenodo.4604647
title: Database for C24Hm (m=0,6,12,18,24)

description: The database contains 5 directories for C24Hn (n=0,6,12,18,24), each of them with the - SCC-DFTB optimized geometries (in angstroms) of the isomers and absolute energies ( in Hartree) : these are the *.xyz files - the parameters characterizing the isomers (full_data*.txt files) - the ionization potentials of all computed structures in the *ionisation.txt files. A Readme.txt file has been added to the datafiles.
DOI: 10.5281/zenodo.4607112
title: Yoon et al 2021 PIC data

description: The files are in hdf5 format. Refer to https://smileipic.github.io/Smilei/post-processing.html for information on how to post-process the data. Fields0.h5 contain information on electromagnetic fields, and current and charge densities. ParticleBinning0.h5 contain information on ion distribution in (x,px,pz) space. ParticleBinning1-6.h5 contain information on elements of the ion pressure tensor, (Txx,Tyy,Tzz,Txy,Tyz,Yzx). ParticleBinning7-13.h5 corresponds to ParticleBinning0-6.h5, but for electrons.
DOI: 10.5281/zenodo.4607111
title: Yoon et al 2021 PIC data

description: The files are in hdf5 format. Refer to https://smileipic.github.io/Smilei/post-processing.html for information on how to post-process the data. Fields0.h5 contain information on electromagnetic fields, and current and charge densities. ParticleBinning0.h5 contain information on ion distribution in (x,px,pz) space. ParticleBinning1-6.h5 contain information on elements of the ion pressure tensor, (Txx,Tyy,Tzz,Txy,Tyz,Yzx). ParticleBinning7-13.h5 corresponds to ParticleBinning0-6.h5, but for electrons.
DOI: 10.6084/m9.figshare.14221847
title: Co-creation data

description: Data collected via a survey methodology from over 400 participants to assess a conceptual model on psycho-social motivators for susceptibility to brand value co-creation
DOI: 10.6084/m9.figshare.14221847.v1
title: Co-creation data

description: Data collected via a survey methodology from over 400 participants to assess a conceptual model on psycho-social motivators for susceptibility to brand value co-creation
DOI: 10.5281/zenodo.4607212
title: J. Shao et al., Fuel 225 (2018) 370–380

description: C2H4 - 16 atm - phi=1
DOI: 10.5281/zenodo.4605412
title: J. Shao et al., Fuel 225 (2018) 370–380

description: C2H4 - 16 atm - phi=1
DOI: 10.5281/zenodo.4549223
title: Wrong versions Datasets

description: All datasets used to estimate <em>w</em>MelPop relative density and average Octomom copy number and to perform data visualizations and statistical analyzes.
description: AR = Arabia BR = RC (Republic of the Congo) BO = Bolivia CA = China SE = USA SF = France WM = w1118-MP (w1118 line infected with wMelPop) CS = w1118-CS (w1118 line infeced with wMel-CS)
DOI: 10.5281/zenodo.4288851
title: Wrong versions Datasets

description: All datasets used to estimate <em>w</em>MelPop relative density and average Octomom copy number and to perform data visualizations and statistical analyzes.
description: AR = Arabia BR = RC (Republic of the Congo) BO = Bolivia CA = China SE = USA SF = France WM = w1118-MP (w1118 line infected with wMelPop) CS = w1118-CS (w1118 line infeced with wMel-CS)
DOI: 10.5281/zenodo.4584629
title: Dataset for "Charting the Complete Thermodynamic Landscape of Gas Adsorption for a Responsive Metal–Organic Framework"

description: Supporting data for: “Charting the complete thermodynamic landscape of gas adsorption for a responsive metal-organic framework”, R. Goeminne, S. Krause, S. Kaskel, T. Verstraelen and J. D Evans, <strong>2021</strong>, <em>J. Am. Chem. Soc.</em>, DOI: 10.1021/jacs.1c00522 <strong>Contents of repository</strong> Input files for the Hybrid GCMC simulations Osmotic potential energy surfaces from the Hybrid GCMC simulations
DOI: 10.5281/zenodo.4584628
title: Dataset for "Charting the Complete Thermodynamic Landscape of Gas Adsorption for a Responsive Metal–Organic Framework"

description: Supporting data for: “Charting the complete thermodynamic landscape of gas adsorption for a responsive metal-organic framework”, R. Goeminne, S. Krause, S. Kaskel, T. Verstraelen and J. D Evans, <strong>2021</strong>, <em>J. Am. Chem. Soc.</em>, DOI: 10.1021/jacs.1c00522 <strong>Contents of repository</strong> Input files for the Hybrid GCMC simulations Osmotic potential energy surfaces from the Hybrid GCMC simulations
DOI: 10.5517/ccdc.csd.cc265fwq
title: CCDC 2031023: Experimental Crystal Structure Determination

description: Related Article: Maxime De Abreu, Philippe Belmont, Etienne Brachet|2021|J.Org.Chem.|86|3758|doi:10.1021/acs.joc.0c02540
DOI: 10.5281/zenodo.4607242
title: J. Shao et al., Fuel 225 (2018) 370–380

description: C2H4 - 16 atm - phi=2
DOI: 10.5281/zenodo.4607241
title: J. Shao et al., Fuel 225 (2018) 370–380

description: C2H4 - 16 atm - phi=2
DOI: 10.21227/t6y1-b936
title: Similar day Approach

description: The forecasting method used is based on Similar Day Approach. The algorithm used sauch days for training which are similar to the day for which forecast has to be done. Similar days are found out using L2 norm considering the load as well as weather profiles. Only those are used for training are close with respect to both- load profile wise and weather wise. Forecast values are found out using by fitting a linear regression model on this trainign dataset.
DOI: 10.17633/rd.brunel.13862912
title: Loneliness in the time of COVID dataset

description: The attached .csv file contains data obtained from an online survey available between to participants between November 2020 and January 2021. The principle outcome variable is loneliness and the principle exposure is chronotype.<br>For more information please contact ray.norbury@brunel.ac.uk<br>
DOI: 10.5281/zenodo.4607263
title: J. Shao et al., Fuel 225 (2018) 370–380

description: C2H4 - 60 atm - phi=2
DOI: 10.5281/zenodo.4607262
title: J. Shao et al., Fuel 225 (2018) 370–380

description: C2H4 - 60 atm - phi=2
DOI: 10.14278/rodare.882
title: ESFR-SMART WP1.2.2 dataset - DecayHeat

description: I/O for ESFR-SMART WP1.2.2 (DecayHeat)
DOI: 10.14278/rodare.881
title: ESFR-SMART WP1.2.2 dataset - DecayHeat

description: I/O for ESFR-SMART WP1.2.2 (DecayHeat)
DOI: 10.17633/rd.brunel.13862912.v1
title: Loneliness in the time of COVID dataset

description: The attached .csv file contains data obtained from an online survey available between to participants between November 2020 and January 2021. The principle outcome variable is loneliness and the principle exposure is chronotype.<br>For more information please contact ray.norbury@brunel.ac.uk<br>
DOI: 10.6084/m9.figshare.13565168.v3
title: TGIF-DB datasets

description: Sequences and homology search results for TGIF-DB.
DOI: 10.1285/i9788883051692p1
title: Frontespizio e pagine iniziali
DOI: 10.1285/i9788883051692p3
title: Indice
DOI: 10.1285/i9788883051692p5
title: Introduzione: Nascere e mettere al mondo al tempo del Grande Reset
DOI: 10.1285/i9788883051692p13
title: Parto e nascita: i molteplici scenari di una storia complessa
DOI: 10.1285/i9788883051692p23
title: L'assistenza al parto nella società tradizionale salentina
DOI: 10.1285/i9788883051692p35
title: I movimenti per la nascita e per l'alimentazione naturali
DOI: 10.1285/i9788883051692p41
title: Los movimientos de la alimentación y del parto naturales
DOI: 10.1285/i9788883051692p47
title: La maternità nei tempi moderni
DOI: 10.1285/i9788883051692p59
title: Violenza ostetrica, una forma di violenza di genere durante la gravidanza e il parto
DOI: 10.1285/i9788883051692p67
title: La violencia obstétrica, una forma de violencia de género durante embarazo y parto
DOI: 10.1285/i9788883051692p75
title: Postvittimismo e assertività femminista
DOI: 10.1285/i9788883051692p79
title: Dire politicamente la nascita, svelare i trucchi del capitale. Filosofe del Novecento di fronte alla nascita