cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/yv429t5h6r.1
title: Technology crowdfunding projects dataset

description: This is an example of a dataset of technology crowdfunding projects collected from the Kickstarter crowdfunding platform. Dataset consists of 1004 projects-502 successful projects and 502 failed ones, extracted from the Kickstarter site (https://www.kickstarter.com/), with (https://www.kickstarter.com/discover/advanced?state=successful&category_id=16&woe_id=23424977&sort=newest&seed=2667028&page=1) as the initial URL. We used statistical methods to analyze and visualize the real data of technology projects.
DOI: 10.6084/m9.figshare.13200977
title: R0221_20180423a_videos

description: Rat 0221, skilled reaching data collected in Leventhal laboratory by Alexandra Bova
DOI: 10.17632/sbrbbn7skn.1
title: Eastern Amazon leaf wetness sensor profile_Binks et al. 2020 Agr.For.Met.
DOI: 10.17632/d3x2w9p439.2
title: White button mushroom RGB-D Images

description: This dataset consists of 1150 pairs of RGB and depth images. The RGB and depth images are aligned. The annotation files contain corresponding bounding boxes of white button mushroom.
DOI: 10.17632/pm5v6pwkkg.3
title: Genome-wide screens implicate loss of cullin ring ligase 3 in persistent proliferation and genome instability in TP53 deficient cells

description: TP53 deficiency is the most common alteration in cancer, however, alone is typically insufficient to drive tumorigenesis. To identify genes promoting tumorigenesis in combination with TP53 deficiency, we performed genome-wide CRISPR/Cas9 knockout screens coupled with proliferation and transformation assays in isogenic cell lines. Loss of several known tumor suppressor genes enhanced cellular proliferation and transformation. Surprisingly, loss neddylation pathway genes, promoted uncontrolled proliferation exclusively in TP53-deficient cells. CUL3 coupled with TP53 loss results in an oncogenic transcriptional program governed by the NFκB, AP-1 and TGF-β pathways. This program maintains persistent cellular proliferation, induces partial epithelial to mesenchymal transition, increases DNA damage, genomic instability, and the rate chromosomal rearrangements. Our findings reveal CUL3 loss as a key event stimulating persistent proliferation in TP53 deficient cells. These findings may be clinically relevant, since TP53-CUL3 deficient cells are highly sensitive to ATM inhibition, exposing a vulnerability that could be exploited for cancer treatment.
DOI: 10.17632/tpw4c3g9m3.1
title: Area postrema cell types that mediate nausea-associated behaviors
DOI: 10.6084/m9.figshare.13145822.v1
title: Homo sapiens network data

description: Data for <i>Homo sapiens</i> networks produced by ssNET: https://github.com/kj-intbio/ssnet <br> Ssnet takes a BioGRID file of functional interaction data for a species and produces a probabilistic functional integrated network. <br> Files types: <b>ssNET.txt</b> - Single source probabilistic functional integrated network <b>BioSystemNet.txt</b> - Probabilistic functional integrated network for <i>Caenorhabditis elegans </i>scored using the BioSystems database for comparison <b>LLSLTP.txt & LLSHTP.txt </b>- ssNet loglikelihood scores for the low throughput (LTP) and high throughput (HTP) data <b>ExtractionData.txt</b> - Extraction data for the <i>Caenorhabditis elegans</i> BioGRID data showing dataset sizes <b>aucs.txt</b> - Area under curve for functional prediction of GO biological processes for ssNET in comparison to the BioSystems scored network in the format: GO term -&gt; ssNet AUC -&gt; BioSystems AUC
DOI: 10.17632/72dwy7tdkd.1
title: Data on Upstream Segment of a Hydrocarbon Supply Chain

description: These files provide data related to different activities in the hydrocarbon supply chain (HCSC). HCSC structures from Crude oil and natural gas supply chains. There are three main sectors in the HCSC related to production areas, processing plants, and demand terminals.
DOI: 10.6084/m9.figshare.13145237
title: Drosophila melanogaster network data

description: Data for <i>Drosophila melanogaster</i> networks produced by ssNET: https://github.com/kj-intbio/ssnet<br>Ssnet takes a BioGRID file of functional interaction data for a species and produces a probabilitistic functional integrated network.<br>Files types:<b>ssNET.txt</b> - Single source probabilistic functional integrated network<b>BioSystemNet.txt</b> - Probabilistic functional integrated network for <i>Caenorhabditis elegans </i>scored using the BioSystems database for comparison<b>LLSLTP.txt & LLSHTP.txt </b>- ssNet loglikelihood scores for the low throughput (LTP) and high throughput (HTP) data<b>ExtractionData.txt</b> - Extraction data for the <i>Caenorhabditis elegans</i> BioGRID data showing dataset sizes<b>aucs.txt</b> - Area under curve for functional prediction of GO biological processes for ssNET in comparison to the BioSystems scored network in the format: GO term -&gt; ssNet AUC -&gt; BioSystems AUC
DOI: 10.6084/m9.figshare.13145237.v1
title: Drosophila melanogaster network data

description: Data for <i>Drosophila melanogaster</i> networks produced by ssNET: https://github.com/kj-intbio/ssnet<br>Ssnet takes a BioGRID file of functional interaction data for a species and produces a probabilitistic functional integrated network.<br>Files types:<b>ssNET.txt</b> - Single source probabilistic functional integrated network<b>BioSystemNet.txt</b> - Probabilistic functional integrated network for <i>Caenorhabditis elegans </i>scored using the BioSystems database for comparison<b>LLSLTP.txt & LLSHTP.txt </b>- ssNet loglikelihood scores for the low throughput (LTP) and high throughput (HTP) data<b>ExtractionData.txt</b> - Extraction data for the <i>Caenorhabditis elegans</i> BioGRID data showing dataset sizes<b>aucs.txt</b> - Area under curve for functional prediction of GO biological processes for ssNET in comparison to the BioSystems scored network in the format: GO term -&gt; ssNet AUC -&gt; BioSystems AUC
DOI: 10.17632/3dxv7mzb76.1
title: Quick COVID-19 Healers Sustain Anti-SARS-CoV-2 Antibody Production, Chen et al

description: Source data for figures, including antibody sequences as described in the manuscript.
DOI: 10.15468/bs8xsq
title: New species and records of Evanioidea and Stephanoidea from New Caledonia (Hymenoptera)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Ramage, Thibault, Jouault, Corentin (2020): New species and records of Evanioidea and Stephanoidea from New Caledonia (Hymenoptera). European Journal of Taxonomy 723: 1-32, DOI: https://doi.org/10.5852/ejt.2020.723.1141
DOI: 10.6084/m9.figshare.13144910
title: Mus musculus network data

description: Data for <i>Mus musculus</i> networks produced by ssNET: https://github.com/kj-intbio/ssnet<br>Ssnet takes a BioGRID file of functional interaction data for a species and produces a probabilitistic functional integrated network.<br>Files types:<b>ssNET.txt</b> - Single source probabilistic functional integrated network<b>BioSystemNet.txt</b> - Probabilistic functional integrated network for <i>Caenorhabditis elegans </i>scored using the BioSystems database for comparison<b>LLSLTP.txt & LLSHTP.txt </b>- ssNet loglikelihood scores for the low throughput (LTP) and high throughput (HTP) data<b>ExtractionData.txt</b> - Extraction data for the <i>Caenorhabditis elegans</i> BioGRID data showing dataset sizes<b>aucs.txt</b> - Area under curve for functional prediction of GO biological processes for ssNET in comparison to the BioSystems scored network in the format: GO term -&gt; ssNet AUC -&gt; BioSystems AUC
DOI: 10.17632/vj24br7gnt.3
title: Major and trace elements in sediments and soils of the Zerafshon area, north - west Tajikistan

description: The data set contains 8816 numerical values regarding the content of 9 major elements as oxides: SiO2, TiO2, Al2O3, FeO, MnO2, MgO, CaO, K2O, and Na2O (in %) and 29 trace elements: Sc, V, Cr, Co, Ni, Zn, As, Br, Rb, Sr, Zr, Sb, Cs, Ba, La, Ce, Nd, Sm, Eu, Gd, Tb, Tm, Yb, Hf, Ta, W, Hg, Th, and U (in mg/kg) as determined by Epithermal Neutron Activation Analysis in 116 samples of sediments and soils, corresponding to 116 different sampling points at an altitude between 883 and 3629 m, all along the Tajik cectoer of Zarafshon river and its tributaries.
DOI: 10.17632/h8rs6v4dgt.3
title: Enhancement of Intracellular Calcium Ion Mobilization by Surface Charges

description: Intracellular calcium ion mobilization as well as cell viability and proliferation can be controlled with surface charges.
DOI: 10.6084/m9.figshare.13144823
title: Arabidosis thaliana network data

description: Data for <i>Arabidopsis thaliana</i> networks produced by ssNET: https://github.com/kj-intbio/ssnet<br>Ssnet takes a BioGRID file of functional interaction data for a species and produces a probabilitistic functional integrated network.<br>Files types:<b>ssNET.txt</b> - Single source probabilistic functional integrated network<b>BioSystemNet.txt</b> - Probabilistic functional integrated network for <i>Caenorhabditis elegans </i>scored using the BioSystems database for comparison<b>LLSLTP.txt & LLSHTP.txt </b>- ssNet loglikelihood scores for the low throughput (LTP) and high throughput (HTP) data<b>ExtractionData.txt</b> - Extraction data for the <i>Caenorhabditis elegans</i> BioGRID data showing dataset sizes<b>aucs.txt</b> - Area under curve for functional prediction of GO biological processes for ssNET in comparison to the BioSystems scored network in the format: GO term -&gt; ssNet AUC -&gt; BioSystems AUC
DOI: 10.6084/m9.figshare.13144823.v1
title: Arabidosis thaliana network data

description: Data for <i>Arabidopsis thaliana</i> networks produced by ssNET: https://github.com/kj-intbio/ssnet<br>Ssnet takes a BioGRID file of functional interaction data for a species and produces a probabilitistic functional integrated network.<br>Files types:<b>ssNET.txt</b> - Single source probabilistic functional integrated network<b>BioSystemNet.txt</b> - Probabilistic functional integrated network for <i>Caenorhabditis elegans </i>scored using the BioSystems database for comparison<b>LLSLTP.txt & LLSHTP.txt </b>- ssNet loglikelihood scores for the low throughput (LTP) and high throughput (HTP) data<b>ExtractionData.txt</b> - Extraction data for the <i>Caenorhabditis elegans</i> BioGRID data showing dataset sizes<b>aucs.txt</b> - Area under curve for functional prediction of GO biological processes for ssNET in comparison to the BioSystems scored network in the format: GO term -&gt; ssNet AUC -&gt; BioSystems AUC
DOI: 10.6084/m9.figshare.13144910.v1
title: Mus musculus network data

description: Data for <i>Mus musculus</i> networks produced by ssNET: https://github.com/kj-intbio/ssnet<br>Ssnet takes a BioGRID file of functional interaction data for a species and produces a probabilitistic functional integrated network.<br>Files types:<b>ssNET.txt</b> - Single source probabilistic functional integrated network<b>BioSystemNet.txt</b> - Probabilistic functional integrated network for <i>Caenorhabditis elegans </i>scored using the BioSystems database for comparison<b>LLSLTP.txt & LLSHTP.txt </b>- ssNet loglikelihood scores for the low throughput (LTP) and high throughput (HTP) data<b>ExtractionData.txt</b> - Extraction data for the <i>Caenorhabditis elegans</i> BioGRID data showing dataset sizes<b>aucs.txt</b> - Area under curve for functional prediction of GO biological processes for ssNET in comparison to the BioSystems scored network in the format: GO term -&gt; ssNet AUC -&gt; BioSystems AUC
DOI: 10.6084/m9.figshare.13145822
title: Homo sapiens network data

description: Data for <i>Homo sapiens</i> networks produced by ssNET: https://github.com/kj-intbio/ssnet <br> Ssnet takes a BioGRID file of functional interaction data for a species and produces a probabilistic functional integrated network. <br> Files types: <b>ssNET.txt</b> - Single source probabilistic functional integrated network <b>BioSystemNet.txt</b> - Probabilistic functional integrated network for <i>Caenorhabditis elegans </i>scored using the BioSystems database for comparison <b>LLSLTP.txt & LLSHTP.txt </b>- ssNet loglikelihood scores for the low throughput (LTP) and high throughput (HTP) data <b>ExtractionData.txt</b> - Extraction data for the <i>Caenorhabditis elegans</i> BioGRID data showing dataset sizes <b>aucs.txt</b> - Area under curve for functional prediction of GO biological processes for ssNET in comparison to the BioSystems scored network in the format: GO term -&gt; ssNet AUC -&gt; BioSystems AUC
DOI: 10.6084/m9.figshare.13144523
title: Caenorhabditis elegans network data

description: Data for <i>Caenorhabditis elegans</i> networks produced by ssNET: https://github.com/kj-intbio/ssnet<br>Ssnet takes a BioGRID file of functional interaction data for a species and produces a probabilitistic functional integrated network.<br>Files types:<b>ssNET.txt</b> - Single source probabilistic functional integrated network<b>BioSystemNet.txt</b> - Probabilistic functional integrated network for <i>Caenorhabditis elegans </i>scored using the BioSystems database for comparison<b>LLSLTP.txt & LLSHTP.txt </b>- ssNet loglikelihood scores for the low throughput (LTP) and high throughput (HTP) data<b>ExtractionData.txt</b> - Extraction data for the <i>Caenorhabditis elegans</i> BioGRID data showing dataset sizes<b>aucs.txt</b> - Area under curve for functional prediction of GO biological processes for ssNET in comparison to the BioSystems scored network in the format: GO term -&gt; ssNet AUC -&gt; BioSystems AUC
DOI: 10.6084/m9.figshare.13144523.v1
title: Caenorhabditis elegans network data

description: Data for <i>Caenorhabditis elegans</i> networks produced by ssNET: https://github.com/kj-intbio/ssnet<br>Ssnet takes a BioGRID file of functional interaction data for a species and produces a probabilitistic functional integrated network.<br>Files types:<b>ssNET.txt</b> - Single source probabilistic functional integrated network<b>BioSystemNet.txt</b> - Probabilistic functional integrated network for <i>Caenorhabditis elegans </i>scored using the BioSystems database for comparison<b>LLSLTP.txt & LLSHTP.txt </b>- ssNet loglikelihood scores for the low throughput (LTP) and high throughput (HTP) data<b>ExtractionData.txt</b> - Extraction data for the <i>Caenorhabditis elegans</i> BioGRID data showing dataset sizes<b>aucs.txt</b> - Area under curve for functional prediction of GO biological processes for ssNET in comparison to the BioSystems scored network in the format: GO term -&gt; ssNet AUC -&gt; BioSystems AUC
DOI: 10.5281/zenodo.4252588
title: SEG/EAGE 3-D Overthrust Models.

description: These are the models available from https://wiki.seg.org/wiki/SEG/EAGE_Salt_and_Overthrust_Models Only uploading here to get a DOI. Copyright notice Copyright 1997 Society of Exploration Geophysicists License This model is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of the license, visit https://creativecommons.org/licenses/by/4.0 or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. The license permits any user to freely copy and redistribute the material in any medium or format. Users are free to remix, transform, and build upon the material for any purpose, including commercially. Refer to LICENSE.txt for all terms and conditions, as well as the disclaimer of warranties. Please DO NOT separate the model and data from this license and ALWAYS include the above statement and link with the data. Acknowledgments This model was created by the SEG/EAGE 3-D Modeling committee, co-chaired by Fred Aminzadeh (University of Southern California) and Fabio Rocca (Politecinio di Milano). Chairpersons of the working groups included Kay Wyatt (Phillips Petroleum), Patrick Lailly (IFP), Norm Burkhard (Lawrence Livermore National Laboratory), Tim Kunz (Amoco), Jun Hua Chen (Chevron) and Max Mulder (TNO). Researchers involved in the creation of this model further include Jean Brac, Pierre Duclos, Laurence Nicoletis, Jean-Claude Lecomte and Alain Bamberger (all IFP), as well as Jane Long (Lawrence Livermore National Laboratory). This model was added to the SEG Open Data collection with help from Joseph Dellinger (BP), Ted Bakamjian (SEG), Dieter Werthmüller (TU Delft) and Philipp Witte (Georgia Institute of Technology). References Aminzadeh, F., Brac, J., Kunz, T., 1997. SEG/EAGE 3-D Salt and Overthrust Models. SEG/EAGE 3-D Modeling Series, No. 1: Distribution CD of Salt and Overthrust models, SEG book series. Aminzadeh, F., Burkhard, N., Long, J., Kunz, T., Duclos, P., 1996. Three dimensional SEG/EAGE models - An Update. The Leading Edge, Vol. 15, 2. Aminzadeh, F., Burkhard, N., Kunz, T., Nicoletis, L., Rocca, F., 1995. 3-D Modeling Project: 3rd Report. The Leading Edge, Vol. 14, 2. Aminzadeh, F., Burkhard, N., Kunz, T., Nicoletis, L., Rocca, F., 1994. SEG/ EAGE 3-D Modeling Project: 2nd Update. The Leading Edge, Vol. 13, 9. Lecomte, J.-C., Campbell, E., Letouzey, J. 1994. Building the SEG/EAGE Overthrust Velocity Macro Model. EAGE/SEG Summer Workshop - Construction of 3-D Macro Velocity Depth Models. 3D Model Representation and Visualization.
DOI: 10.5281/zenodo.4252587
title: SEG/EAGE 3-D Overthrust Models.

description: These are the models available from https://wiki.seg.org/wiki/SEG/EAGE_Salt_and_Overthrust_Models Only uploading here to get a DOI. Copyright notice Copyright 1997 Society of Exploration Geophysicists License This model is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of the license, visit https://creativecommons.org/licenses/by/4.0 or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. The license permits any user to freely copy and redistribute the material in any medium or format. Users are free to remix, transform, and build upon the material for any purpose, including commercially. Refer to LICENSE.txt for all terms and conditions, as well as the disclaimer of warranties. Please DO NOT separate the model and data from this license and ALWAYS include the above statement and link with the data. Acknowledgments This model was created by the SEG/EAGE 3-D Modeling committee, co-chaired by Fred Aminzadeh (University of Southern California) and Fabio Rocca (Politecinio di Milano). Chairpersons of the working groups included Kay Wyatt (Phillips Petroleum), Patrick Lailly (IFP), Norm Burkhard (Lawrence Livermore National Laboratory), Tim Kunz (Amoco), Jun Hua Chen (Chevron) and Max Mulder (TNO). Researchers involved in the creation of this model further include Jean Brac, Pierre Duclos, Laurence Nicoletis, Jean-Claude Lecomte and Alain Bamberger (all IFP), as well as Jane Long (Lawrence Livermore National Laboratory). This model was added to the SEG Open Data collection with help from Joseph Dellinger (BP), Ted Bakamjian (SEG), Dieter Werthmüller (TU Delft) and Philipp Witte (Georgia Institute of Technology). References Aminzadeh, F., Brac, J., Kunz, T., 1997. SEG/EAGE 3-D Salt and Overthrust Models. SEG/EAGE 3-D Modeling Series, No. 1: Distribution CD of Salt and Overthrust models, SEG book series. Aminzadeh, F., Burkhard, N., Long, J., Kunz, T., Duclos, P., 1996. Three dimensional SEG/EAGE models - An Update. The Leading Edge, Vol. 15, 2. Aminzadeh, F., Burkhard, N., Kunz, T., Nicoletis, L., Rocca, F., 1995. 3-D Modeling Project: 3rd Report. The Leading Edge, Vol. 14, 2. Aminzadeh, F., Burkhard, N., Kunz, T., Nicoletis, L., Rocca, F., 1994. SEG/ EAGE 3-D Modeling Project: 2nd Update. The Leading Edge, Vol. 13, 9. Lecomte, J.-C., Campbell, E., Letouzey, J. 1994. Building the SEG/EAGE Overthrust Velocity Macro Model. EAGE/SEG Summer Workshop - Construction of 3-D Macro Velocity Depth Models. 3D Model Representation and Visualization.
DOI: 10.17632/w9sn6vhkst.1
title: Oxidative Generation of Lithium Nucleation Sites Stabilizes Li-Metal Electrodes (2020)

description: Each folder contains the raw data for the corresponding image presented in the article. Some SEM images were processed to remove impurities to allow more focus on the features we would like to show.
DOI: 10.17632/k6hgwtzjhw.1
title: East Asian Monsoon changes early in the last deglaciation and insights into the interpretation of oxygen isotope changes in the Chinese stalagmite record

description: Dataset for SM7 sample from Shima (Stone Horse) Cave, Hunan Province, Central China.
DOI: 10.17632/czfr4bcpmp.1
title: Data for: A Method for Detecting Perfluorooctanoic Acid and Perfluorooctane Sulfonate in Water Samples Using Genetically Engineered Bacterial Biosensor
DOI: 10.17632/fy4b5dtw9g.1
title: Data for: A Thermodynamic Evolution of Magnetite Oxygen carrier via Chemical Looping Reforming of Methane

description: A Thermodynamic Evolution of Magnetite Oxygen carrier via Chemical Looping Reforming of Methane
DOI: 10.17632/gm566p4ttt.1
title: Data for: Enhancing structures for joint climate change mitigation and adaptation action in city administrations - empirical insights and practical implications

description: Increasing impacts from climate change have prompted international calls for the development of synergetic mitigation and adaptation policies and measures. While cities are seen as key actors in the implementation of related actions, there is a lack of scientific knowledge on the organizational conditions required to achieve this in practice. Our paper addresses this gap. Specifically, we assess the impact of various organizational configurations on the initiation of joint mitigation and adaptation resolutions by city councils in Germany. Our results demonstrate that the joint organizational institutionalization of mitigation and adaptation (i.e. joint departments) can be considered both as a necessary and significant prerequisite for joint implementation, unlike joint climate action plans. The developed methodology and identified conditions present an innovative way forward to assess and improve the initiation of integrated resolutions. Our work contributes to organizational and climate policy integration theories, and can help cities worldwide to optimize their organizational configurations and enhance joint mitigation and adaptation actions.
DOI: 10.6084/m9.figshare.9171992
title: Data from: The best bang for the bucks: Rethinking global investment priorities in species conservation

description: Original data from: Cordero, S., G.J. Castaño-Villa & F.E. Fontúrbel (2020) the best bang for the bucks: Rethinking global investment priorities in species conservation. Sustainability 12: 9252. doi: 10.3390/su.12219252<br>This repository entry contains the following files:<br>(1) database.txt - Our full database including socio-economic variables and threatened species data.(2) 01-Core_analyses.Rmd - Original code (in R Markdown format) used for obtaining the results.(3) 01-Core_analyses.pdf - Knitted PDF from the Rmd file.(4) 02-Database_facts.Rmd - Database further exploration (information not included in the paper).(5) 02-Database_facts.pdf - Knitted PDF from the Rmd file.(6) multiplot.R - Supplementary R function needed to run part of the code.
DOI: 10.6084/m9.figshare.9171992.v2
title: Data from: The best bang for the bucks: Rethinking global investment priorities in species conservation

description: Original data from: Cordero, S., G.J. Castaño-Villa & F.E. Fontúrbel (2020) the best bang for the bucks: Rethinking global investment priorities in species conservation. Sustainability 12: 9252. doi: 10.3390/su.12219252<br>This repository entry contains the following files:<br>(1) database.txt - Our full database including socio-economic variables and threatened species data.(2) 01-Core_analyses.Rmd - Original code (in R Markdown format) used for obtaining the results.(3) 01-Core_analyses.pdf - Knitted PDF from the Rmd file.(4) 02-Database_facts.Rmd - Database further exploration (information not included in the paper).(5) 02-Database_facts.pdf - Knitted PDF from the Rmd file.(6) multiplot.R - Supplementary R function needed to run part of the code.