The Experts below are selected from a list of 144708 Experts worldwide ranked by ideXlab platform
Bernhard Steubing - One of the best experts on this subject based on the ideXlab platform.
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the ecoinvent Database Version 3 part i overview and methodology
International Journal of Life Cycle Assessment, 2016Co-Authors: Gregor Wernet, Jürgen Reinhard, Emilia Morenoruiz, Bernhard Steubing, Christian Bauer, Bo Pedersen WeidemaAbstract:Purpose Good background data are an important requirement in LCA. Practitioners generally make use of LCI Databases for such data, and the ecoinvent Database is the largest transparent unit-process LCI Database worldwide. Since its first release in 2003, it has been continuously updated, and Version 3 was published in 2013. The release of Version 3 introduced several significant methodological and technological improvements, besides a large number of new and updated datasets. The aim was to expand the content of the Database, set the foundation for a truly global Database, support regionalized LCIA, offer multiple system models, allow for easier integration of data from different regions, and reduce maintenance efforts. This article describes the methodological developments.
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the ecoinvent Database Version 3 part ii analyzing lca results and comparison to Version 2
International Journal of Life Cycle Assessment, 2016Co-Authors: Jürgen Reinhard, Gregor Wernet, Bernhard Steubing, Christian Bauer, Emilia MorenoruizAbstract:Version 3 of ecoinvent includes more data, new modeling principles, and, for the first time, several system models: the “Allocation, cut-off by classification” (Cut-off) system model, which replicates the modeling principles of Version 2, and two newly introduced models called “Allocation at the point of substitution” (APOS) and “Consequential” (Wernet et al. 2016). The aim of this paper is to analyze and explain the differences in life cycle impact assessment (LCIA) results of the v3.1 Cut-off system model in comparison to v2.2 as well as the APOS and Consequential system models. In order to do this, functionally equivalent datasets were matched across Database Versions and LCIA results compared to each other. In addition, the contribution of specific sectors was analyzed. The importance of new and updated data as well as new modeling principles is illustrated through examples. Differences were observed in between all Database Versions using the impact assessment methods Global Warming Potential (GWP100a), ReCiPe Endpoint (H/A), and Ecological Scarcity 2006 (ES’06). The highest differences were found for the comparison of the v3.1 Cut-off and v2.2. At average, LCIA results increased by 6, 8, and 17 % and showed a median dataset deviation of 13, 13, and 21 % for GWP, ReCiPe, and ES’06, respectively. These changes are due to the simultaneous update and addition of new data as well as through the introduction of global coverage and spatially consistent linking of activities throughout the Database. As a consequence, supply chains are now globally better represented than in Version 2 and lead, e.g., in the electricity sector, to more realistic life cycle inventory (LCI) background data. LCIA results of the Cut-off and APOS models are similar and differ mainly for recycling materials and wastes. In contrast, LCIA results of the Consequential Version differ notably from the attributional system models, which is to be expected due to fundamentally different modeling principles. The use of marginal instead of average suppliers in markets, i.e., consumption mixes, is the main driver for result differences. LCIA results continue to change as LCI Databases evolve, which is confirmed by a historical comparison of v1.3 and v2.2. Version 3 features more up-to-date background data as well as global supply chains and should, therefore, be used instead of previous Versions. Continuous efforts will be required to decrease the contribution of Rest-of-the-World (RoW) productions and thereby improve the global coverage of supply chains.
Emilia Morenoruiz - One of the best experts on this subject based on the ideXlab platform.
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the ecoinvent Database Version 3 part i overview and methodology
International Journal of Life Cycle Assessment, 2016Co-Authors: Gregor Wernet, Jürgen Reinhard, Emilia Morenoruiz, Bernhard Steubing, Christian Bauer, Bo Pedersen WeidemaAbstract:Purpose Good background data are an important requirement in LCA. Practitioners generally make use of LCI Databases for such data, and the ecoinvent Database is the largest transparent unit-process LCI Database worldwide. Since its first release in 2003, it has been continuously updated, and Version 3 was published in 2013. The release of Version 3 introduced several significant methodological and technological improvements, besides a large number of new and updated datasets. The aim was to expand the content of the Database, set the foundation for a truly global Database, support regionalized LCIA, offer multiple system models, allow for easier integration of data from different regions, and reduce maintenance efforts. This article describes the methodological developments.
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the ecoinvent Database Version 3 part ii analyzing lca results and comparison to Version 2
International Journal of Life Cycle Assessment, 2016Co-Authors: Ernhard Steubing, Jurge Reinhard, Grego Werne, Christia Aue, Emilia MorenoruizAbstract:Purpose Version 3 of ecoinvent includes more data, new modeling principles, and, for the first time, several system models: the “Allocation, cut-off by classification” (Cut-off) system model, which replicates the modeling principles of Version 2, and two newly introduced models called “Allocation at the point of substitution” (APOS) and “Consequential” (Wernet et al. 2016). The aim of this paper is to analyze and explain the differences in life cycle impact assessment (LCIA) results of the v3.1 Cut-off system model in comparison to v2.2 as well as the APOS and Consequential system models.
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the ecoinvent Database Version 3 part ii analyzing lca results and comparison to Version 2
International Journal of Life Cycle Assessment, 2016Co-Authors: Jürgen Reinhard, Gregor Wernet, Bernhard Steubing, Christian Bauer, Emilia MorenoruizAbstract:Version 3 of ecoinvent includes more data, new modeling principles, and, for the first time, several system models: the “Allocation, cut-off by classification” (Cut-off) system model, which replicates the modeling principles of Version 2, and two newly introduced models called “Allocation at the point of substitution” (APOS) and “Consequential” (Wernet et al. 2016). The aim of this paper is to analyze and explain the differences in life cycle impact assessment (LCIA) results of the v3.1 Cut-off system model in comparison to v2.2 as well as the APOS and Consequential system models. In order to do this, functionally equivalent datasets were matched across Database Versions and LCIA results compared to each other. In addition, the contribution of specific sectors was analyzed. The importance of new and updated data as well as new modeling principles is illustrated through examples. Differences were observed in between all Database Versions using the impact assessment methods Global Warming Potential (GWP100a), ReCiPe Endpoint (H/A), and Ecological Scarcity 2006 (ES’06). The highest differences were found for the comparison of the v3.1 Cut-off and v2.2. At average, LCIA results increased by 6, 8, and 17 % and showed a median dataset deviation of 13, 13, and 21 % for GWP, ReCiPe, and ES’06, respectively. These changes are due to the simultaneous update and addition of new data as well as through the introduction of global coverage and spatially consistent linking of activities throughout the Database. As a consequence, supply chains are now globally better represented than in Version 2 and lead, e.g., in the electricity sector, to more realistic life cycle inventory (LCI) background data. LCIA results of the Cut-off and APOS models are similar and differ mainly for recycling materials and wastes. In contrast, LCIA results of the Consequential Version differ notably from the attributional system models, which is to be expected due to fundamentally different modeling principles. The use of marginal instead of average suppliers in markets, i.e., consumption mixes, is the main driver for result differences. LCIA results continue to change as LCI Databases evolve, which is confirmed by a historical comparison of v1.3 and v2.2. Version 3 features more up-to-date background data as well as global supply chains and should, therefore, be used instead of previous Versions. Continuous efforts will be required to decrease the contribution of Rest-of-the-World (RoW) productions and thereby improve the global coverage of supply chains.
David M Mcgaughey - One of the best experts on this subject based on the ideXlab platform.
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eye in a disk eyeintegration human pan eye and body transcriptome Database Version 1 0
Investigative Ophthalmology & Visual Science, 2019Co-Authors: Vinay S Swamy, David M McgaugheyAbstract:Purpose We develop an accessible and reliable RNA sequencing (RNA-seq) transcriptome Database of healthy human eye tissues and a matching reactive web application to query gene expression in eye and body tissues. Methods We downloaded the raw sequence data for 1375 RNA-seq samples across 54 tissues in the Genotype-Tissue Expression (GTEx) project as a noneye reference set. We then queried several public repositories to find all healthy, nonperturbed, human eye-related tissue RNA-seq samples. The 916 eye and 1375 GTEx samples were sent into a Snakemake-based reproducible pipeline we wrote to quantify all known transcripts and genes, removes samples with poor sequence quality and mislabels, normalizes expression values across each tissue, perform 882 differential expression tests, calculate GO term enrichment, and output all as a single SQLite Database file: the Eye in a Disk (EiaD) dataset. Furthermore, we rewrote the web application eyeIntegration (available in the public domain at https://eyeIntegration.nei.nih.gov) to display EiaD. Results The new eyeIntegration portal provides quick visualization of human eye-related transcriptomes published to date by Database Version, gene/transcript, 19 eye tissues, and 54 body tissues. As a test of the value of this unified pan-eye dataset, we showed that fetal and organoid retina are highly similar at a pan-transcriptome level, but display distinct differences in certain pathways and gene families, such as protocadherin and HOXB family members. Conclusions The eyeIntegration v1.0 web app serves the pan-human eye and body transcriptome dataset, EiaD. This offers the eye community a powerful and quick means to test hypotheses on human gene and transcript expression across 54 body and 19 eye tissues.
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eye in a disk eyeintegration human pan eye and body transcriptome Database Version 1 0
bioRxiv, 2019Co-Authors: Vinay S Swamy, David M McgaugheyAbstract:PURPOSE To develop an easily accessible and reliable RNA-seq transcriptome Database of healthy human eye tissues and a matching reactive web application to query gene expression in eye and body tissues. METHODS We downloaded the raw sequnce data for 892 RNA-seq samples across 44 tissues in the GTEx project as a non-eye reference set. We then queried several public repositories to find all healthy, non-perturbed, human eye-related tissue RNA-seq samples. The 1311 total samples we found were sent into a Snakemake-based reproducible pipeline we wrote to quantify all known transcripts and genes, removes samples with poor sequence quality and mislabels, normalizes expression values across each tissue, performs 450 differential expression tests, calculates GO term enrichment, and outputs all as a single SQLite Database file: the Eye in a Disk (EiaD) dataset. Furthermore, we rewrote the web application eyeIntegration (https://eyeIntegration.nei.nih.gov) to display EiaD. RESULTS The new eyeIntegration portal provides quick visualization of human eye-related transcriptomes published to date, by Database Version, gene/transcript, and 58 tissues. As a test of the value of this unified pan-eye dataset, we showed that fetal and organoid retina are highly similar at a pan-transcriptome level but display distinct differences in certain pathways and gene families like protocadherin and HOXB family members. CONCLUSION The eyeIntegration v1.0 web app serves the pan-human eye and body transcriptome dataset, EiaD. This offers the eye community a powerful and quick mean to testing hypotheses on human gene and transcript expression across 44 body and 14 eye tissues.
Richard L Prager - One of the best experts on this subject based on the ideXlab platform.
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the society of thoracic surgeons adult cardiac surgery Database Version 2 73 more is better
The Annals of Thoracic Surgery, 2015Co-Authors: Terry Shih, Gaetano Paone, Patricia F Theurer, Donna Mcdonald, David M Shahian, Richard L PragerAbstract:Background With the introduction of Version 2.73, several new patient risk factors are now captured in The Society of Thoracic Surgeons' (STS) Adult Cardiac Surgery Database. We sought to evaluate the potential association of these risk factors with mortality. Methods We reviewed all patients with an STS predicted risk of mortality in our statewide quality collaborative Database from July 2011 to September 2013 (N = 19,743). Univariate analyses were used to determine significant associations between mortality and the new risk factors in Version 2.73. We then performed multivariable analysis, incorporating the STS predicted risk of mortality into our regression. Results In the univariate model, patients with illicit drug use, syncope, unresponsive neurologic state, cancer within the last 5 years, current smoking history, other tobacco use, or sleep apnea had no significant difference in mortality ( p > 0.05). Patients with liver disease, elevated Model for End-Stage Liver Disease score, mediastinal radiation, prolonged 5-meter walk test, home oxygen use, inhaled medications or bronchodilator therapy, decreased forced expiratory volume, and history of recent pneumonia had significant increases in operative mortality ( p Conclusions Several of the new STS data variables were significantly associated with operative mortality after cardiac surgery. The addition of these patient factors improves our understanding of evolving patient demographics and comorbid conditions and their impact on perioperative risk. This will improve both shared decision making and assessments of provider performance.
Christian Bauer - One of the best experts on this subject based on the ideXlab platform.
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the integration of long term marginal electricity supply mixes in the ecoinvent consequential Database Version 3 4 and examination of modeling choices
International Journal of Life Cycle Assessment, 2019Co-Authors: Christian Bauer, Christopher L. Mutel, Laurent Vandepaer, Karin Treyer, Ben AmorAbstract:The long-term marginal electricity supply mixes of 40 countries were generated and integrated into Version 3.4 of the ecoinvent consequential Database. The total electricity production originating from these countries accounts for 77% of the current global electricity generation. The goal of this article is to provide an overview of the methodology used to calculate the marginal mixes and to evaluate the influence of key parameters and methodological choices on the results. The marginal mixes are based on public energy projections from national and international authorities and reflect the accumulated effect of changes in demand for electricity on the installation and operation of new-generation capacities. These newly generated marginal mixes are first examined in terms of their compositions and environmental impacts. They are then compared to several sets of alternative electricity supply mixes calculated using different methodological choices or data sources. Renewable energy sources (RES) as well as natural gas power plants show the highest growth rates and usually dominate the marginal mixes. Nevertheless, important variations may exist between the marginal mixes of the different countries in terms of their technological compositions and environmental impacts. The examination of the modeling choices reveals substantial variations between the marginal mixes integrated into the ecoinvent consequential Database Version 3.4 and marginal mixes generated using alternative modeling options. These different modeling possibilities include changes in the methodology, temporal parameters, and the underlying energy scenarios. Furthermore, in most of the impact categories, average (i.e., attributional) mixes cause higher impact scores than marginal mixes due to higher shares of RES in marginal mixes. Accurate and consistent data for electricity supply is integrated into a consequential Database providing a strong basis for the development of consequential Life Cycle Assessments. The methodology adopted in this Version of the Database eliminates several shortcomings from the previous approach which led to unrealistic marginal mixes in several countries. The use of energy scenarios allows the evolution of the electricity system to be considered within the definition of the marginal mixes. The modeling choices behind the electricity marginal mix should be adjusted to the goal and scope of individual studies and their influence on the results evaluated.
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the ecoinvent Database Version 3 part i overview and methodology
International Journal of Life Cycle Assessment, 2016Co-Authors: Gregor Wernet, Jürgen Reinhard, Emilia Morenoruiz, Bernhard Steubing, Christian Bauer, Bo Pedersen WeidemaAbstract:Purpose Good background data are an important requirement in LCA. Practitioners generally make use of LCI Databases for such data, and the ecoinvent Database is the largest transparent unit-process LCI Database worldwide. Since its first release in 2003, it has been continuously updated, and Version 3 was published in 2013. The release of Version 3 introduced several significant methodological and technological improvements, besides a large number of new and updated datasets. The aim was to expand the content of the Database, set the foundation for a truly global Database, support regionalized LCIA, offer multiple system models, allow for easier integration of data from different regions, and reduce maintenance efforts. This article describes the methodological developments.
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the ecoinvent Database Version 3 part ii analyzing lca results and comparison to Version 2
International Journal of Life Cycle Assessment, 2016Co-Authors: Jürgen Reinhard, Gregor Wernet, Bernhard Steubing, Christian Bauer, Emilia MorenoruizAbstract:Version 3 of ecoinvent includes more data, new modeling principles, and, for the first time, several system models: the “Allocation, cut-off by classification” (Cut-off) system model, which replicates the modeling principles of Version 2, and two newly introduced models called “Allocation at the point of substitution” (APOS) and “Consequential” (Wernet et al. 2016). The aim of this paper is to analyze and explain the differences in life cycle impact assessment (LCIA) results of the v3.1 Cut-off system model in comparison to v2.2 as well as the APOS and Consequential system models. In order to do this, functionally equivalent datasets were matched across Database Versions and LCIA results compared to each other. In addition, the contribution of specific sectors was analyzed. The importance of new and updated data as well as new modeling principles is illustrated through examples. Differences were observed in between all Database Versions using the impact assessment methods Global Warming Potential (GWP100a), ReCiPe Endpoint (H/A), and Ecological Scarcity 2006 (ES’06). The highest differences were found for the comparison of the v3.1 Cut-off and v2.2. At average, LCIA results increased by 6, 8, and 17 % and showed a median dataset deviation of 13, 13, and 21 % for GWP, ReCiPe, and ES’06, respectively. These changes are due to the simultaneous update and addition of new data as well as through the introduction of global coverage and spatially consistent linking of activities throughout the Database. As a consequence, supply chains are now globally better represented than in Version 2 and lead, e.g., in the electricity sector, to more realistic life cycle inventory (LCI) background data. LCIA results of the Cut-off and APOS models are similar and differ mainly for recycling materials and wastes. In contrast, LCIA results of the Consequential Version differ notably from the attributional system models, which is to be expected due to fundamentally different modeling principles. The use of marginal instead of average suppliers in markets, i.e., consumption mixes, is the main driver for result differences. LCIA results continue to change as LCI Databases evolve, which is confirmed by a historical comparison of v1.3 and v2.2. Version 3 features more up-to-date background data as well as global supply chains and should, therefore, be used instead of previous Versions. Continuous efforts will be required to decrease the contribution of Rest-of-the-World (RoW) productions and thereby improve the global coverage of supply chains.
