cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc25880r
title: CCDC 2003933: Experimental Crystal Structure Determination

description: Related Article: Hongshuai Gao,  Gang Zhang|2020|Eur.J.Org.Chem.|2020|5455|doi:10.1002/ejoc.202000871
DOI: 10.5517/ccdc.csd.cc25881s
title: CCDC 2003934: Experimental Crystal Structure Determination

description: Related Article: Hongshuai Gao,  Gang Zhang|2020|Eur.J.Org.Chem.|2020|5455|doi:10.1002/ejoc.202000871
DOI: 10.5517/ccdc.csd.cc25882t
title: CCDC 2003935: Experimental Crystal Structure Determination

description: Related Article: Hongshuai Gao,  Gang Zhang|2020|Eur.J.Org.Chem.|2020|5455|doi:10.1002/ejoc.202000871
DOI: 10.5517/ccdc.csd.cc25883v
title: CCDC 2003936: Experimental Crystal Structure Determination

description: Related Article: Hongshuai Gao,  Gang Zhang|2020|Eur.J.Org.Chem.|2020|5455|doi:10.1002/ejoc.202000871
DOI: 10.5517/ccdc.csd.cc25884w
title: CCDC 2003937: Experimental Crystal Structure Determination

description: Related Article: Hongshuai Gao,  Gang Zhang|2020|Eur.J.Org.Chem.|2020|5455|doi:10.1002/ejoc.202000871
DOI: 10.5517/ccdc.csd.cc25885x
title: CCDC 2003938: Experimental Crystal Structure Determination

description: Related Article: Hongshuai Gao,  Gang Zhang|2020|Eur.J.Org.Chem.|2020|5455|doi:10.1002/ejoc.202000871
DOI: 10.5517/ccdc.csd.cc25886y
title: CCDC 2003939: Experimental Crystal Structure Determination

description: Related Article: Hongshuai Gao,  Gang Zhang|2020|Eur.J.Org.Chem.|2020|5455|doi:10.1002/ejoc.202000871
DOI: 10.5517/ccdc.csd.cc25887z
title: CCDC 2003940: Experimental Crystal Structure Determination

description: Related Article: Hongshuai Gao,  Gang Zhang|2020|Eur.J.Org.Chem.|2020|5455|doi:10.1002/ejoc.202000871
DOI: 10.5517/ccdc.csd.cc258880
title: CCDC 2003941: Experimental Crystal Structure Determination

description: Related Article: Hongshuai Gao,  Gang Zhang|2020|Eur.J.Org.Chem.|2020|5455|doi:10.1002/ejoc.202000871
DOI: 10.5063/r78cmb
title: Granivory in California sage scrub: implications for common plant invaders and ecosystem conservation

description: Data was collected to quantify the relative roles of California sage scrub granivores (ant, rodents, and birds) for seed removal using seed dish experiments and motion-sensor video observation. Further, we recorded seed removal for four common sage scrub plant species: small-seeded invasive mustard Brassica nigra, large seeded invasive grass Bromus rubens, native large-seeded shrub Encelia californica, and small-seeded native shrub Salvia apiana. The experiment was conducted at the Robert J. Bernard Field Station, a 35-hectare field station located in Claremont, California, from November 2017 to November 2018. A manuscript detailing the results from this study is under review (July 2021) in the journal Plant Ecology.
DOI: 10.15468/batay6
title: Two new species of Selitrichodes (Hymenoptera: Eulophidae: Tetrastichinae) inducing galls on Casuarina (Casuarinaceae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Fisher, Nicole, Moore, Aubrey, Brown, Bradley, Purcell, Matthew, Taylor, Gary S., Salle, John La (2014): Two new species of Selitrichodes (Hymenoptera: Eulophidae: Tetrastichinae) inducing galls on Casuarina (Casuarinaceae). Zootaxa 3790 (4): 534-542, DOI: http://dx.doi.org/10.11646/zootaxa.3790.4.2
DOI: 10.6084/m9.figshare.14925102
title: Table 38. DCs Keywords Themes frequency Hits and connectivity counts [Quant. articles]

description: DCs Keywords Themes frequency Hits and connectivity counts [Quant. articles]<b></b>
DOI: 10.15468/7xn7yv
title: Revision of the Neotropical caddisfly genus Phylloicus (Trichoptera: Calamoceratidae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Prather Table Of Contents, Aysha L. (2003): Revision of the Neotropical caddisfly genus Phylloicus (Trichoptera: Calamoceratidae). Zootaxa 275 (1): 1-214, DOI: 10.11646/zootaxa.275.1.1, URL: https://biotaxa.org/Zootaxa/article/view/zootaxa.275.1.1
DOI: 10.5281/zenodo.5057609
title: "@alex, this fixes #9": Analysis of Referencing Patterns in Pull Request Discussions

description: This publication consists of a dataset of 7k references manually identified in 450 Pull request (PR) discussion threads sampled from GitHub in CSV format. In addition to the dataset, it also contains R code files which were written to analyze this dataset statistically. This dataset is released under the research, which is accepted for publication at CSCW 2021 conference, titled "@alex, this fixes #9": Analysis of Referencing Patterns in Pull Request Discussions". <strong>Paper Abstract</strong> Pull Requests (PRs) are a frequently used method for proposing changes to source code repositories. When discussing proposed changes in a PR discussion, stakeholders often reference a wide variety of information objects for establishing shared awareness and common ground. Previous work has not considered how referential behavior impacts collaborative software development via PRs. This knowledge gap is the major barrier in evaluating the current support for referencing in PRs and improving them. We conducted an explorative analysis of ~7K references, collected from 450 public PRs on GitHub, and constructed taxonomies of referent types and expressions. Using our annotated dataset, we identified several patterns in the use of references. Referencing source code elements was prevalent but the authoring interface lacks support for it. Three classes of contextual factors influence referencing behaviors: referent type, discussion thread, and project attributes. Referencing patterns may indicate PR outcomes (e.g., merged PRs frequently reference issues, users, and tests). We conclude with design implications to support more effective referencing in PR discussion interfaces.
DOI: 10.5281/zenodo.4895094
title: Three-Dimensional Hydrodynamic Simulations of Convective Nuclear Burning In Massive Stars Near Iron Core Collapse

description: Data products from ApJ article Three-Dimensional Hydrodynamic Simulations of Convective Nuclear Burning In Massive Stars Near Iron Core Collapse, 2021. Four 3D core-collapse supernova progenitor models. Works that utilize these progenitor models are required to cite article. The models were evolved to times listed in Table 1 of the article, the collapse time according to the 1D MESA model. All 3D data are in FLASH4 format using the HDF5 data structure.
description: For questions regarding the dataset contact: c a r l n o t s a g a n (at) l a n l (dot) g o v.
DOI: 10.5281/zenodo.4895093
title: Three-Dimensional Hydrodynamic Simulations of Convective Nuclear Burning In Massive Stars Near Iron Core Collapse

description: Data products from ApJ article Three-Dimensional Hydrodynamic Simulations of Convective Nuclear Burning In Massive Stars Near Iron Core Collapse, 2021. Four 3D core-collapse supernova progenitor models. Works that utilize these progenitor models are required to cite article. The models were evolved to times listed in Table 1 of the article, the collapse time according to the 1D MESA model. All 3D data are in FLASH4 format using the HDF5 data structure.
description: For questions regarding the dataset contact: c a r l n o t s a g a n (at) l a n l (dot) g o v.
DOI: 10.6084/m9.figshare.14925180
title: The Therapeutic Potential of Bedside Art Observation in Hematologic Cancer Inpatients: A Randomized Controlled Trial

description: The Therapeutic Potential of Bedside Art Observation in Hematologic Cancer Inpatients: A Randomized Controlled Trial
DOI: 10.6084/m9.figshare.14921670
title: A squeezed quantum microcomb on a chip. Data for figure 2 to 6

description: These are all the raw data for the figure 2 to 6 in "A squeezed quantum microcomb on a chip".
DOI: 10.6084/m9.figshare.14921670.v1
title: A squeezed quantum microcomb on a chip. Data for figure 2 to 6

description: These are all the raw data for the figure 2 to 6 in "A squeezed quantum microcomb on a chip".
DOI: 10.5517/ccdc.csd.cc258891
title: CCDC 2003942: Experimental Crystal Structure Determination

description: Related Article: Hongshuai Gao,  Gang Zhang|2020|Eur.J.Org.Chem.|2020|5455|doi:10.1002/ejoc.202000871
DOI: 10.5517/ccdc.csd.cc2589lc
title: CCDC 2003982: Experimental Crystal Structure Determination

description: Related Article: Janusz Gregolinski,  Katarzyna Ślepokura  Andrzej Bil  Jerzy Lisowski|2020|Eur.J.Org.Chem.|2020|5714|doi:10.1002/ejoc.202000919
DOI: 10.5517/ccdc.csd.cc2589md
title: CCDC 2003983: Experimental Crystal Structure Determination

description: Related Article: Janusz Gregolinski,  Katarzyna Ślepokura  Andrzej Bil  Jerzy Lisowski|2020|Eur.J.Org.Chem.|2020|5714|doi:10.1002/ejoc.202000919
DOI: 10.5517/ccdc.csd.cc2589nf
title: CCDC 2003984: Experimental Crystal Structure Determination

description: Related Article: Janusz Gregolinski,  Katarzyna Ślepokura  Andrzej Bil  Jerzy Lisowski|2020|Eur.J.Org.Chem.|2020|5714|doi:10.1002/ejoc.202000919
DOI: 10.5517/ccdc.csd.cc258gtr
title: CCDC 2004144: Experimental Crystal Structure Determination

description: Related Article: Miaoling Huang, Rongxing Qiu, Zhonghua Pan, Dan Tian, Yunwen Tao, Jinqing Lin, Geng‐Geng Luo|2020|Eur.J.Inorg.Chem.|2020|4313|doi:10.1002/ejic.202000767
DOI: 10.5517/ccdc.csd.cc258qjq
title: CCDC 2004383: Experimental Crystal Structure Determination

description: Related Article: Saurabh Kumar,  Joel T. Mague,  Maravanji S. Balakrishna|2020|Eur.J.Inorg.Chem.|2020|2718|doi:10.1002/ejic.202000520
DOI: 10.5517/ccdc.csd.cc258w5j
title: CCDC 2004527: Experimental Crystal Structure Determination

description: Related Article: Jared M. Ashtree, Ivana Borilović, Kuduva R. Vignesh, Abinash Swain, Sarah H. Hamilton, Yasmin L. Whyatt, Sophie L. Benjamin, Wasinee Phonsri, Craig M. Forsyth, Wolfgang Wernsdorfer, Alessandro Soncini, Gopalan Rajaraman, Stuart K. Langley, Keith S. Murray|2021|Eur.J.Inorg.Chem.|2021|435|doi:10.1002/ejic.202001082
DOI: 10.5517/ccdc.csd.cc258qkr
title: CCDC 2004384: Experimental Crystal Structure Determination

description: Related Article: Saurabh Kumar,  Joel T. Mague,  Maravanji S. Balakrishna|2020|Eur.J.Inorg.Chem.|2020|2718|doi:10.1002/ejic.202000520
DOI: 10.5517/ccdc.csd.cc258qls
title: CCDC 2004385: Experimental Crystal Structure Determination

description: Related Article: Saurabh Kumar,  Joel T. Mague,  Maravanji S. Balakrishna|2020|Eur.J.Inorg.Chem.|2020|2718|doi:10.1002/ejic.202000520
DOI: 10.5517/ccdc.csd.cc258qnv
title: CCDC 2004387: Experimental Crystal Structure Determination

description: Related Article: Saurabh Kumar,  Joel T. Mague,  Maravanji S. Balakrishna|2020|Eur.J.Inorg.Chem.|2020|2718|doi:10.1002/ejic.202000520
DOI: 10.5517/ccdc.csd.cc258s7h
title: CCDC 2004436: Experimental Crystal Structure Determination

description: Related Article: Si-Jia Zhou,  Meng Sun,  Jing-Yi Wang,  Xian-Yang Yu,  Han Lu,  Yu-Chen Zhang,  Feng Shi|2020|Eur.J.Org.Chem.|2020|4301|doi:10.1002/ejoc.202000693
DOI: 10.5517/ccdc.csd.cc258sdn
title: CCDC 2004441: Experimental Crystal Structure Determination

description: Related Article: Si-Jia Zhou,  Meng Sun,  Jing-Yi Wang,  Xian-Yang Yu,  Han Lu,  Yu-Chen Zhang,  Feng Shi|2020|Eur.J.Org.Chem.|2020|4301|doi:10.1002/ejoc.202000693
DOI: 10.5517/ccdc.csd.cc258zdv
title: CCDC 2004627: Experimental Crystal Structure Determination

description: Related Article: Hou-Ze Gui,  Zhe Meng,  Zhan-Shuai Xiao,  Ze-Ren Yang,  Yin Wei,  Min Shi|2020|Eur.J.Org.Chem.|2020|6614|doi:10.1002/ejoc.202001146
DOI: 10.5517/ccdc.csd.cc2590k2
title: CCDC 2004663: Experimental Crystal Structure Determination

description: Related Article: Jared M. Ashtree, Ivana Borilović, Kuduva R. Vignesh, Abinash Swain, Sarah H. Hamilton, Yasmin L. Whyatt, Sophie L. Benjamin, Wasinee Phonsri, Craig M. Forsyth, Wolfgang Wernsdorfer, Alessandro Soncini, Gopalan Rajaraman, Stuart K. Langley, Keith S. Murray|2021|Eur.J.Inorg.Chem.|2021|435|doi:10.1002/ejic.202001082
DOI: 10.5517/ccdc.csd.cc2590m4
title: CCDC 2004665: Experimental Crystal Structure Determination

description: Related Article: Yanting Huang, Wenjun Jiang, Xin Xi, Yan Li, Xiaoping Wang, Ming-Chung Yang, Zheng-Feng Zhang, Ming-Der Su, Hongping Zhu|2020|Eur.J.Inorg.Chem.|2020|3496|doi:10.1002/ejic.202000506
DOI: 10.5517/ccdc.csd.cc2591l4
title: CCDC 2004695: Experimental Crystal Structure Determination

description: Related Article: HuiJing Koay, Mohammad B. Haskali, Peter D. Roselt, Jonathan M. White, Paul Stephen Donnelly|2020|Eur.J.Inorg.Chem.|2020|3378|doi:10.1002/ejic.202000547
DOI: 10.5517/ccdc.csd.cc2591m5
title: CCDC 2004696: Experimental Crystal Structure Determination

description: Related Article: HuiJing Koay, Mohammad B. Haskali, Peter D. Roselt, Jonathan M. White, Paul Stephen Donnelly|2020|Eur.J.Inorg.Chem.|2020|3378|doi:10.1002/ejic.202000547
DOI: 10.5517/ccdc.csd.cc2591n6
title: CCDC 2004697: Experimental Crystal Structure Determination

description: Related Article: HuiJing Koay, Mohammad B. Haskali, Peter D. Roselt, Jonathan M. White, Paul Stephen Donnelly|2020|Eur.J.Inorg.Chem.|2020|3378|doi:10.1002/ejic.202000547
DOI: 10.5517/ccdc.csd.cc2591p7
title: CCDC 2004698: Experimental Crystal Structure Determination

description: Related Article: HuiJing Koay, Mohammad B. Haskali, Peter D. Roselt, Jonathan M. White, Paul Stephen Donnelly|2020|Eur.J.Inorg.Chem.|2020|3378|doi:10.1002/ejic.202000547
DOI: 10.5517/ccdc.csd.cc2592sc
title: CCDC 2004732: Experimental Crystal Structure Determination

description: Related Article: Gernot P. Weinberger, Florian Sommer, Ana Torvisco, Roland C. Fischer, Michaela Flock|2020|Eur.J.Inorg.Chem.|2020|3778|doi:10.1002/ejic.202000581
DOI: 10.5517/ccdc.csd.cc2592td
title: CCDC 2004733: Experimental Crystal Structure Determination

description: Related Article: Gernot P. Weinberger, Florian Sommer, Ana Torvisco, Roland C. Fischer, Michaela Flock|2020|Eur.J.Inorg.Chem.|2020|3778|doi:10.1002/ejic.202000581
DOI: 10.5517/ccdc.csd.cc2597g6
title: CCDC 2004877: Experimental Crystal Structure Determination

description: Related Article: Lukas Tendera, Thomas Schaub, Mirjam J. Krahfuss, Maximilian W. Kuntze‐Fechner, Udo Radius|2020|Eur.J.Inorg.Chem.|2020|3194|doi:10.1002/ejic.202000493
DOI: 10.5517/ccdc.csd.cc2597h7
title: CCDC 2004878: Experimental Crystal Structure Determination

description: Related Article: Lukas Tendera, Thomas Schaub, Mirjam J. Krahfuss, Maximilian W. Kuntze‐Fechner, Udo Radius|2020|Eur.J.Inorg.Chem.|2020|3194|doi:10.1002/ejic.202000493
DOI: 10.5517/ccdc.csd.cc2597j8
title: CCDC 2004879: Experimental Crystal Structure Determination

description: Related Article: Lukas Tendera, Thomas Schaub, Mirjam J. Krahfuss, Maximilian W. Kuntze‐Fechner, Udo Radius|2020|Eur.J.Inorg.Chem.|2020|3194|doi:10.1002/ejic.202000493
DOI: 10.5517/ccdc.csd.cc2597k9
title: CCDC 2004880: Experimental Crystal Structure Determination

description: Related Article: Lukas Tendera, Thomas Schaub, Mirjam J. Krahfuss, Maximilian W. Kuntze‐Fechner, Udo Radius|2020|Eur.J.Inorg.Chem.|2020|3194|doi:10.1002/ejic.202000493
DOI: 10.5517/ccdc.csd.cc2597lb
title: CCDC 2004881: Experimental Crystal Structure Determination

description: Related Article: Lukas Tendera, Thomas Schaub, Mirjam J. Krahfuss, Maximilian W. Kuntze‐Fechner, Udo Radius|2020|Eur.J.Inorg.Chem.|2020|3194|doi:10.1002/ejic.202000493
DOI: 10.5517/ccdc.csd.cc2597mc
title: CCDC 2004882: Experimental Crystal Structure Determination

description: Related Article: Lukas Tendera, Thomas Schaub, Mirjam J. Krahfuss, Maximilian W. Kuntze‐Fechner, Udo Radius|2020|Eur.J.Inorg.Chem.|2020|3194|doi:10.1002/ejic.202000493
DOI: 10.5517/ccdc.csd.cc23kjs0
title: CCDC 1953272: Experimental Crystal Structure Determination

description: Related Article: Jérôme Bayardon,  Benjamin Rousselle,  Yoann Rousselin,  Quentin Bonnin,  Raluca Malacea-Kabbara|2020|Eur.J.Org.Chem.|2020|4723|doi:10.1002/ejoc.202000723
DOI: 10.5517/ccdc.csd.cc2597nd
title: CCDC 2004883: Experimental Crystal Structure Determination

description: Related Article: Lukas Tendera, Thomas Schaub, Mirjam J. Krahfuss, Maximilian W. Kuntze‐Fechner, Udo Radius|2020|Eur.J.Inorg.Chem.|2020|3194|doi:10.1002/ejic.202000493
DOI: 10.5517/ccdc.csd.cc23kjt1
title: CCDC 1953273: Experimental Crystal Structure Determination

description: Related Article: Jérôme Bayardon,  Benjamin Rousselle,  Yoann Rousselin,  Quentin Bonnin,  Raluca Malacea-Kabbara|2020|Eur.J.Org.Chem.|2020|4723|doi:10.1002/ejoc.202000723
DOI: 10.5281/zenodo.5080610
title: Data for Quantifying the Impact of Parametric Uncertainty on Automatic Mechanism Generation for CO2 Hydrogenation on Ni(111)

description: Data, scripts, and all generated mechanisms for the preprint and article "Quantifying the Impact of Parametric Uncertainty on Automatic Mechanism Generation for CO<sub>2</sub> Hydrogenation on Ni(111)"
DOI: 10.5281/zenodo.4662851
title: Data for Quantifying the Impact of Parametric Uncertainty on Automatic Mechanism Generation for CO2 Hydrogenation on Ni(111)

description: Data, scripts, and all generated mechanisms for the preprint and article "Quantifying the Impact of Parametric Uncertainty on Automatic Mechanism Generation for CO<sub>2</sub> Hydrogenation on Ni(111)"
DOI: 10.3886/e142861v1
title: A Natural Language Processing Approach to Measuring Treatment Adherence and Consistency Using Semantic Similarity

description: This project contains code for calculating semantic similarity measures of treatment adherence and replicability, as described in the AERA Open article, A Natural Language Processing Approach to Measuring Treatment Adherence and Consistency Using Semantic Similarity.
DOI: 10.6084/m9.figshare.14697029
title: Bayesian Optimization Via Barrier Functions

description: Hybrid optimization methods that combine statistical modeling with mathematical programming have become a popular solution for Bayesian optimization (BO) because they can better leverage both the efficient local search properties of the numerical method and the global search properties of the statistical model. These methods seek to create a sequential design strategy for efficiently optimizing expensive black-box functions when gradient information is not readily available. In this article, we propose a novel BO strategy that combines response surface modeling with barrier methods to efficiently solve expensive constrained optimization problems in computer modeling. At the heart of all BO algorithms is an acquisition function for effectively guiding the search. Our hybrid algorithm is guided by a novel acquisition function that tries to decrease the objective function as much as possible while simultaneously trying to ensure that the boundary of the constraint space is never crossed. Illustrations highlighting the success of our method are provided, including a real-world computer model optimization experiment from hydrology. Supplementary files for this article are available online.
DOI: 10.6084/m9.figshare.14697029.v2
title: Bayesian Optimization Via Barrier Functions

description: Hybrid optimization methods that combine statistical modeling with mathematical programming have become a popular solution for Bayesian optimization (BO) because they can better leverage both the efficient local search properties of the numerical method and the global search properties of the statistical model. These methods seek to create a sequential design strategy for efficiently optimizing expensive black-box functions when gradient information is not readily available. In this article, we propose a novel BO strategy that combines response surface modeling with barrier methods to efficiently solve expensive constrained optimization problems in computer modeling. At the heart of all BO algorithms is an acquisition function for effectively guiding the search. Our hybrid algorithm is guided by a novel acquisition function that tries to decrease the objective function as much as possible while simultaneously trying to ensure that the boundary of the constraint space is never crossed. Illustrations highlighting the success of our method are provided, including a real-world computer model optimization experiment from hydrology. Supplementary files for this article are available online.
DOI: 10.6084/m9.figshare.14697029.v1
title: Bayesian Optimization via Barrier Functions

description: Hybrid optimization methods that combine statistical modeling with mathematical programming have become a popular solution for Bayesian optimization because they can better leverage both the efficient local search properties of the numerical method and the global search properties of the statistical model. These methods seek to create a sequential design strategy for efficiently optimizing expensive black-box functions when gradient information is not readily available. In this article, we propose a novel Bayesian optimization strategy that combines response surface modeling with barrier methods to efficiently solve expensive constrained optimization problems in computer modeling. At the heart of all Bayesian optimization algorithms is an acquisition function for effectively guiding the search. Our hybrid algorithm is guided by a novel acquisition function that tries to decrease the objective function as much as possible while simultaneously trying to ensure that the boundary of the constraint space is never crossed. Illustrations highlighting the success of our method are provided, including a real-world computer model optimization experiment from hydrology. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.3761562.v1912
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.3886/e142861
title: A Natural Language Processing Approach to Measuring Treatment Adherence and Consistency Using Semantic Similarity

description: This project contains code for calculating semantic similarity measures of treatment adherence and replicability, as described in the AERA Open article, A Natural Language Processing Approach to Measuring Treatment Adherence and Consistency Using Semantic Similarity.
DOI: 10.3886/e142861v2
title: A Natural Language Processing Approach to Measuring Treatment Adherence and Consistency Using Semantic Similarity

description: This project contains code for calculating semantic similarity measures of treatment adherence and replicability, as described in the AERA Open article, A Natural Language Processing Approach to Measuring Treatment Adherence and Consistency Using Semantic Similarity.
DOI: 10.25989/e77g-tb66
title: Materials Experiment and Analysis Database: Electrochemistry measurements containing 5631 files performed on plate containing Ni,Co,Fe,La annealed at 350.0C to add O on 2015-02-19 from Run 20150219.113732

description: Files available via S3 at https://renc.osn.xsede.org/ini210004tommorrell/0_gregoire/<br>20150219.113732.copied-20150220105050427PST.zip <a class="btn btn-xs piwik_download" 
            type="application/octet-stream" href="https://renc.osn.xsede.org/ini210004tommorrell/0_gregoire/e77g-tb66/20150219.113732.copied-20150220105050427PST.zip">
            <i class="fa fa-download"></i> Download</a>    <br>
description: This record is a component of the Materials Experiment and
Analysis Database (MEAD). It contains raw data and metadata from millions 
of materials synthesis and characterization experiments, as well as the 
analysis and distillation of that data into property and performance 
metrics. The unprecedented quantity and diversity of experimental data 
are searchable by experiment and analysis attributes generated by both 
researchers and data processing software.