Data in social and behavioral sciences are routinely collected using questionnaires, and each domain of interest is tapped by multiple indicators. Structural equation modeling (SEM) is one of the most widely used methods to analyze such data. However, conventional methods for SEM face difficulty when the number of variables (p) is large even when the sample size (N) is also rather large. This article addresses the issue of model inference with the likelihood ratio statistic Tml. Using the method of empirical modeling, mean-and-variance corrected statistics for SEM with many variables are developed. Results show that the new statistics not only perform much better than Tml but also are substantial improvements over other corrections to Tml. When combined with a robust transformation, the new statistics also perform well with non-normally distributed data.

Background: Attribution to the original contributor upon reuse of published data is important both as a reward for data creators and to document the provenance of research findings. Previous studies have found that papers with publicly available datasets receive a higher number of citations than similar studies without available data. However, few previous analyses have had the statistical power to control for the many variables known to predict citation rate, which has led to uncertain estimates of the "citation benefit". Furthermore, little is known about patterns in data reuse over time and across datasets. Method and Results: Here, we look at citation rates while controlling for many known citation predictors, and investigate the variability of data reuse. In a multivariate regression on 10,555 studies that created gene expression microarray data, we found that studies that made data available in a public repository received 9% (95% confidence interval: 5% to 13%) more citations than similar studies for which the data was not made available. Date of publication, journal impact factor, open access status, number of authors, first and last author publication history, corresponding author country, institution citation history, and study topic were included as covariates. The citation benefit varied with date of dataset deposition: a citation benefit was most clear for papers published in 2004 and 2005, at about 30%. Authors published most papers using their own datasets within two years of their first publication on the dataset, whereas data reuse papers published by third-party investigators continued to accumulate for at least six years. To study patterns of data reuse directly, we compiled 9,724 instances of third party data reuse via mention of GEO or ArrayExpress accession numbers in the full text of papers. The level of third-party data use was high: for 100 datasets deposited in year 0, we estimated that 40 papers in PubMed reused a dataset by year 2, 100 by year 4, and more than 150 data reuse papers had been published by year 5. Data reuse was distributed across a broad base of datasets: a very conservative estimate found that 20% of the datasets deposited between 2003 and 2007 had been reused at least once by third parties. Conclusion: After accounting for other factors affecting citation rate, we find a robust citation benefit from open data, although a smaller one than previously reported. We conclude there is a direct effect of third-party data reuse that persists for years beyond the time when researchers have published most of the papers reusing their own data. Other factors that may also contribute to the citation benefit are considered.We further conclude that, at least for gene expression microarray data, a substantial fraction of archived datasets are reused, and that the intensity of dataset reuse has been steadily increasing since 2003.

The General Household Survey (GHS) was a continuous national survey of people living in private households conducted on an annual basis, by the Social Survey Division of the Office for National Statistics (ONS). The main aim of the survey was to collect data on a range of core topics, covering household, family and individual information. This information was used by government departments and other organisations for planning, policy and monitoring purposes, and to present a picture of households, family and people in Great Britain. From 2008, the General Household Survey became a module of the Integrated Household Survey (IHS). In recognition, the survey was renamed the General Lifestyle Survey (GLF). The GLF closed in 2011.

Secure Access GLF
The Secure Access version includes additional, detailed variables not included in either the standard 'End User Licence' (EUL) version (see under GN 33090). Not all variables are available for all years, but extra variables that can typically be found in the Secure Access version but not in the EUL version relate to:

• geography: postcodes (anonymised prior to 2009)
• employment details, including economic status, self-employment, number of employees
• employment and training schemes
• reason for reduction in income
• looking for work
• benefits
• borrowing money and bill arrears
• nationality
• migration, including when arrived in UK and previous country of residence
• ethnicity
• religious identity

Prospective users of the Secure Access version of the GLF will need to fulfil additional requirements, commencing with the completion of an extra application form to demonstrate to the data owners exactly why they need access to the extra, more detailed variables, in order to obtain permission to use that version. Secure Access users must also complete face-to-face training and agree to Secure Access' User Agreement and Breaches Penalties Policy (see 'Access' section below). Therefore, users are encouraged to download and inspect the EUL version of the data prior to ordering the Secure Access version. Further details and links for all GLF studies available from the UK Data Archive can be found via the General Lifestyle Survey series web page.

Geographical references: postcodes
The postcodes available in the Secure Access version of the data prior to 2009 are pseudo-anonymised postcodes. The real postcodes were not available due to the potential risk of identification of the observations. However, these replacement postcodes retain the inherent nested characteristics of real postcodes, and will allow researchers to aggregate observations to other geographic units, e.g. wards, super output areas, etc. In the dataset, the variable of the replacement postcode is 'new_PC'.

History
The GHS started in 1971 and has been carried out continuously since then, except for breaks in 1997-1998 when the survey was reviewed, and in 1999-2000 when the survey was redeveloped. Following the 1997 review, the survey was relaunched from April 2000 with a different design. The relevant development work and the changes made are fully described in the Living in Britain report for the 2000-2001 survey. Following its review, the GHS was changed to comprise two elements: the continuous survey and extra modules, or 'trailers'. The continuous survey remained unchanged from 2000 to 2004, apart from essential adjustments to take account of, for example, changes in benefits and pensions. The GHS retained its modular structure and this allowed a number of different trailers to be included for each of those years, to a plan agreed by sponsoring government departments.

Further changes to the GHS methodology from 2005
From April 1994 to 2005, the GHS was conducted on a financial year basis, with fieldwork spread evenly from April of one year to March the following year. However, in 2005 the survey period reverted to a calendar year and the whole of the annual sample was surveyed in the nine months from April to December 2005. Future surveys will run from January to December each year, hence the title date change to single year from 2005 onwards. Since the 2005 GHS (EUL version held under SN 5640) does not cover the January-March quarter, this affects annual estimates for topics which are subject to seasonal variation. To rectify this, where the questions were the same in 2005 as in 2004-2005, the final quarter of the latter survey was added (weighted in the correct proportion) to the nine months of the 2005 survey. Furthermore, in 2005, the European Union (EU) made a legal obligation (EU-SILC) for member states to collect additional statistics on income and living conditions. In addition to this the EU-SILC data cover poverty and social exclusion. These statistics are used to help plan and monitor European social policy by comparing poverty indicators and changes over time across the EU. The EU-SILC requirement has been integrated into the GHS, leading to large-scale changes in the 2005 survey questionnaire. The trailers on 'Views of your Local Area' and 'Dental Health' were removed. Other changes were made to many of the standard questionnaire sections, details of which may be found in the GHS 2005 documentation.

Further changes to the GLF methodology from 2008
As noted above, the General Household Survey (GHS) was renamed the General Lifestyle Survey (GLF) in 2008. The sample design is the same as the GHS before, and the questionnaire remains largely the same. The main change is that the GLF then included the IHS core questions, which are common to all of the separate modules that together comprise the IHS. Some of these core questions are simply questions that were previously asked in the same or a similar format on all of the IHS component surveys (including the GLF). The core questions cover employment, smoking prevalence, general health, ethnicity, citizenship and national identity. These questions are asked by proxy if an interview is not possible with the selected respondent (that is a member of the household can answer on behalf of other respondents in the household). This is a departure from the GHS which did not ask smoking prevalence and general health questions by proxy, whereas the GLF does from 2008. For details on other changes to the GLF questionnaire, please see the GLF 2008 documentation.

Changes to the drinking section
There have been a number of revisions to the methodology that is used to produce the alcohol consumption estimates. In 2006, the average number of units assigned to the different drink types and the assumption around the average size of a wine glass was updated, resulting in significantly increased consumption estimates. In addition to the revised method, a new question about wine glass size was included in the survey in 2008. Respondents were asked whether they have consumed small (125 ml), standard (175 ml) or large (250 ml) glasses of wine. The data from this question are used when calculating the number of units of alcohol consumed by the respondent. It is assumed that a small glass contains 1.5 units, a standard glass contains 2 units and a large glass contains 3 units. (In 2006 and 2007 it was assumed that all respondents drank from a standard 175 ml glass containing 2 units.) The datasets contain the original set of variables based on the original methodology, as well as those based on the revised and (for 2008 onwards) updated methodologies. Further details on these changes are provided in the GHS 2006 and GLF/GLS 2008 documentation.

Further information may be found on the ONS GLF webpages.

Correction of erroneous variables in individual 2008 data file
The 'source of income' variables (SrcInc01-14 and SrcIncT1-T5) in the individual file for 2008 have been revised in October 2011 to correct erroneous values in the previous version.

Change in household serial number variable
The household serial number variable 'Hserial' has been replaced by the variable 'HholdId' in the 2008 individual and household files.

The second edition (September 2013) includes data for 2009-2010. Data for 2011 were added in 2017, after the ONS withdrawal of the Special Licence version.

Abstract The objective of this study was to verify possible differences in performance between boys and girls in the Zulliger Comprehensive System (ZSC). The sample consisted of 623 children aged from 6 to 14, from the Southeast region of Brazil, divided into four age groups: six to seven years, eight to nine, ten to eleven, and twelve to fourteen years. The means were compared using the t-test. The results indicated that some differences remained significant even after the Bonferroni correction, although the number of variables was reduced considerably when compared to the literature. The findings are discussed together with studies with projective techniques as well as other personality techniques. It was concluded that, although many variables were corroborated in the literature, more studies with more homogenous samples are needed, including, for example, control for the cognitive level and sociodemographic variables.

Descriptive statistics of new MSK dataset vs. incomplete observed dataset (for more details on the variables, please consult S2 Table).

The variables contained in the data sets are primarily concerned with perinatal outcomes and maternal health. A number of variables with respect to the social and economic status of the mothers and their families were also included (ie. Occupation, Marital status, Region). While all nine data sets are centered around these common themes and hold many variables in common, each data set has a unique combination of variables. The types of fields are wide-ranging but are primarily concerned with infant birth, maternal health, and socioeconomic status. The Dublin patients are a random sample of those found in the clinical records of the hospital. Case files were compiled from two sources, the Register of Patients, which included the administrative record of each patient, and the Master’s Ward Book, which noted the medical circumstances of each case. These records exist in continuous series during the years with which this study is concerned, and only minor changes occurred in the categories of information collected. Most of these documents were held by the Rotunda Hospital when they were consulted for this project, but all of them have now been transferred to the Public Record Office of Ireland in Dublin. As birth weights were first recorded in July 1869, 100 cases were selected for that year. In all subsequent years 200 cases were chosen. The preliminary data base consisted of 12,454 cases. The weight and length means in the sample are accurate to 84 grams and 0.4 centimeter at a confidence level of 95 percent

'amip4xco2' is an experiment of the CMIP5 - Coupled Model Intercomparison Project Phase 5 (https://pcmdi.llnl.gov/mips/cmip5). CMIP5 is meant to provide a framework for coordinated climate change experiments for the next five years and thus includes simulations for assessment in the AR5 as well as others that extend beyond the AR5.

6.5 amip4xco2 (6.5 4xCO2 AMIP) - Version 1: Identical to expt. 6.2b, but with AMIP SSTs prescribed as in expt. 3.3 (which is the control for this run).

Experiment design: https://pcmdi.llnl.gov/mips/cmip5/experiment_design.html List of output variables: https://pcmdi.llnl.gov/mips/cmip5/datadescription.html Output: time series per variable in model grid spatial resolution in netCDF format Earth System model and the simulation information: CIM repository

Entry name/title of data are specified according to the Data Reference Syntax (https://pcmdi.llnl.gov/mips/cmip5/docs/cmip5_data_reference_syntax.pdf) as activity/product/institute/model/experiment/frequency/modeling realm/MIP table/ensemble member/version number/variable name/CMOR filename.nc.

"esmrcp85" is an experiment of the CMIP5 - Coupled Model Intercomparison Project Phase 5 (https://pcmdi.llnl.gov/mips/cmip5). CMIP5 is meant to provide a framework for coordinated climate change experiments for the next five years and thus includes simulations for assessment in the AR5 as well as others that extend beyond the AR5.

5.3 esmrcp85 (5.3 ESM RCP8.5): Future projection (2006-2100) forced by RCP8.5. As in experiment 4.2_RCP8.5 but emissions-forced (with atmospheric CO2 determined by the model itself).

Experiment design: https://pcmdi.llnl.gov/mips/cmip5/experiment_design.html List of output variables: https://pcmdi.llnl.gov/mips/cmip5/datadescription.html Output: time series per variable in model grid spatial resolution in netCDF format Earth System model and the simulation information: CIM repository

Entry name/title of data are specified according to the Data Reference Syntax (https://pcmdi.llnl.gov/mips/cmip5/docs/cmip5_data_reference_syntax.pdf) as activity/product/institute/model/experiment/frequency/modeling realm/MIP table/ensemble member/version number/variable name/CMOR filename.nc.

These are the number of fish species and environmental data used to estimate numbers of fish species in the Western Indian Ocean. The results are published in: Modeling the spatial distribution of numbers of coral reef fish species and community types in the Western Indian Ocean faunal province - https://doi.org/10.3354/meps14538. ABSTRACT: Predicting and mapping coral reef diversity at moderate scales can assist spatial planning and prioritizing conservation activities. We made coarse-scale (6.25 km2) predictive models for numbers of coral reef fish species and community composition starting with a spatially complete database of 70 environmental variables available for 7039 mapped reef cells in the Western Indian Ocean. An ensemble model was created from a process of variable elimination and selectivity to make the best predictions irrespective of human influences. This best model was compared to models using preselected variables commonly used to evaluate climate change and human fishing and water quality influences. Many variables (~27) contributed to the best number of species and community composition models, but local variables of biomass, depth, and retention connectivity were dominant predictors. The key human-influenced variables included fish biomass and distance to human populations, with weaker associations with sediments and nutrients. Climate-influenced variables were generally weaker and included median sea surface temperature (SST) with contributions in declining order from SST kurtosis, bimodality, excess summer heat, SST skewness, SST rate of rise, and coral cover. Community composition variability was best explained by 2 dominant community richness axes of damselfishes–angelfishes and butterflyfishes–parrotfishes. Numbers of damselfish–angelfish species were ecologically separated by depth, and damselfishes declined with increasing depth, median temperature, cumulative excess heat, rate of temperature rise, and chronic temperature stresses. Species of butterflyfish–parrotfish separated by median temperature, and butterflyfish numbers declined with increasing temperature, chronic and acute temperature variability, and the rate of temperature rise. Several fish diversity hotspots were found in the East African Coastal Current Ecoregion centered in Tanzania, followed by Mayotte, southern Kenya, and northern Mozambique. If biomass can be maintained, the broad distributions of species combined with compensatory community responses should maintain high diversity and ecological resilience to climate change and other human stressors.

[ Derived from parent entry - See data hierarchy tab ]

'amip4xco2' is an experiment of the CMIP5 - Coupled Model Intercomparison Project Phase 5 (https://pcmdi.llnl.gov/mips/cmip5). CMIP5 is meant to provide a framework for coordinated climate change experiments for the next five years and thus includes simulations for assessment in the AR5 as well as others that extend beyond the AR5.

6.5 amip4xco2 (6.5 4xCO2 AMIP) - Version 1: Identical to expt. 6.2b, but with AMIP SSTs prescribed as in expt. 3.3 (which is the control for this run).

Experiment design: https://pcmdi.llnl.gov/mips/cmip5/experiment_design.html List of output variables: https://pcmdi.llnl.gov/mips/cmip5/datadescription.html Output: time series per variable in model grid spatial resolution in netCDF format Earth System model and the simulation information: CIM repository

Entry name/title of data are specified according to the Data Reference Syntax (https://pcmdi.llnl.gov/mips/cmip5/docs/cmip5_data_reference_syntax.pdf) as activity/product/institute/model/experiment/frequency/modeling realm/MIP table/ensemble member/version number/variable name/CMOR filename.nc.

"historical" is an experiment of the CMIP5 - Coupled Model Intercomparison Project Phase 5 (https://pcmdi.llnl.gov/mips/cmip5). CMIP5 is meant to provide a framework for coordinated climate change experiments for the next five years and thus includes simulations for assessment in the AR5 as well as others that extend beyond the AR5.

3.2 historical (3.2 Historical): Simulation of recent past (1850 to 2005). Impose changing conditions (consistent with observations).

Experiment design: https://pcmdi.llnl.gov/mips/cmip5/experiment_design.html List of output variables: https://pcmdi.llnl.gov/mips/cmip5/datadescription.html Output: time series per variable in model grid spatial resolution in netCDF format Earth System model and the simulation information: CIM repository

Entry name/title of data are specified according to the Data Reference Syntax (https://pcmdi.llnl.gov/mips/cmip5/docs/cmip5_data_reference_syntax.pdf) as activity/product/institute/model/experiment/frequency/modeling realm/MIP table/ensemble member/version number/variable name/CMOR filename.nc.

[ Derived from parent entry - See data hierarchy tab ]

'amip' is an experiment of the CMIP5 - Coupled Model Intercomparison Project Phase 5 (https://pcmdi.llnl.gov/mips/cmip5). CMIP5 is meant to provide a framework for coordinated climate change experiments for the next five years and thus includes simulations for assessment in the AR5 as well as others that extend beyond the AR5.

3.3 amip (3.3 AMIP) - Version 1: AMIP (1979 - at least 2008). Impose SSTs and sea ice from observations but with other conditions as in experiment 3.2 historical.

Experiment design: https://pcmdi.llnl.gov/mips/cmip5/experiment_design.html List of output variables: https://pcmdi.llnl.gov/mips/cmip5/datadescription.html Output: time series per variable in model grid spatial resolution in netCDF format Earth System model and the simulation information: CIM repository

Entry name/title of data are specified according to the Data Reference Syntax (https://pcmdi.llnl.gov/mips/cmip5/docs/cmip5_data_reference_syntax.pdf) as activity/product/institute/model/experiment/frequency/modeling realm/MIP table/ensemble member/version number/variable name/CMOR filename.nc.

‘historical’ is an experiment of the CMIP5 — Coupled Model Intercomparison Project Phase 5

(https://pcmdi.llnl.gov/mips/cmip5).CMIP5 is meant to provide a framework for coordinated

climate change experiments for the next five years and thus includes simulations for assessment in the AR5 as well as others that extend beyond the AR5.

3.2 historical (3.2 Historical) — Version 1: Simulation of recent past (1850 to 2005). Impose changing conditions (consistent with observations).

Experiment design: https://pcmdi.llnl.gov/mips/cmip5/experiment_design.html

List of output variables: https://pcmdi.llnl.gov/mips/cmip5/datadescription.html

Output: time series per variable in model grid spatial resolution in netCDF format

Earth System Model and the simulation information: CIM repository

Entry name/title of data are specified according to the Data Reference Syntax

https://pcmdi.llnl.gov/mips/cmip5/docs/cmip5_data_reference_syntax.pdf

as activity/product/institute/model/experiment/frequency/modeling realm/MIP table/ensemble

Member/version number/variable name/CMOR filename.nc.

[ Derived from parent entry - See data hierarchy tab ]

aqua4xco2 is an experiment of the CMIP5 - Coupled Model Intercomparison Project Phase 5 ( https://pcmdi.llnl.gov/mips/cmip5 ). CMIP5 is meant to provide a framework for coordinated climate change experiments for the next five years and thus includes simulations for assessment in the AR5 as well as others that extend beyond the AR5.

aqua4xco2 (6.7b 4xCO2 aqua planet) - Version 2: Consistent with CFMIP requirements, impose a 4xCO2 on zonally uniform SSTs of expt. 6.7a (which is the control for this run).

Experiment design: https://pcmdi.llnl.gov/mips/cmip5/experiment_design.html List of output variables: https://pcmdi.llnl.gov/mips/cmip5/datadescription.html Output: time series per variable in model grid spatial resolution in netCDF format Earth System model and the simulation information: CIM repository

Entry name/title of data are specified according to the Data Reference Syntax ( https://pcmdi.llnl.gov/mips/cmip5/docs/cmip5_data_reference_syntax.pdf ) as activity/product/institute/model/experiment/frequency/modeling realm/MIP table/ensemble member/version number/variable name/CMOR filename.nc .

[ Derived from parent entry - See data hierarchy tab ]

'amip' is an experiment of the CMIP5 - Coupled Model Intercomparison Project Phase 5 (https://pcmdi.llnl.gov/mips/cmip5). CMIP5 is meant to provide a framework for coordinated climate change experiments for the next five years and thus includes simulations for assessment in the AR5 as well as others that extend beyond the AR5.

3.3 amip (3.3 AMIP) - Version 1: AMIP (1979 - at least 2008). Impose SSTs and sea ice from observations but with other conditions as in experiment 3.2 historical.

Experiment design: https://pcmdi.llnl.gov/mips/cmip5/experiment_design.html List of output variables: https://pcmdi.llnl.gov/mips/cmip5/datadescription.html Output: time series per variable in model grid spatial resolution in netCDF format Earth System model and the simulation information: CIM repository

Entry name/title of data are specified according to the Data Reference Syntax (https://pcmdi.llnl.gov/mips/cmip5/docs/cmip5_data_reference_syntax.pdf) as activity/product/institute/model/experiment/frequency/modeling realm/MIP table/ensemble member/version number/variable name/CMOR filename.nc.

[ Derived from parent entry - See data hierarchy tab ]

'rcp45' is an experiment of the CMIP5 - Coupled Model Intercomparison Project Phase 5 (https://pcmdi.llnl.gov/mips/cmip5). CMIP5 is meant to provide a framework for coordinated climate change experiments for the next five years and thus includes simulations for assessment in the AR5 as well as others that extend beyond the AR5.

4.1 rcp45 (4.1 RCP4.5) - Version 1: Future projection (2006-2100) forced by RCP4.5. RCP4.5 is a representative concentration pathway which approximately results in a radiative forcing of 4.5 W m-2 at year 2100, relative to pre-industrial conditions. RCPs are time-dependent, consistent projections of emissions and concentrations of radiatively active gases and particles.

Experiment design: https://pcmdi.llnl.gov/mips/cmip5/experiment_design.html List of output variables: https://pcmdi.llnl.gov/mips/cmip5/datadescription.html Output: time series per variable in model grid spatial resolution in netCDF format Earth System model and the simulation information: CIM repository

Entry name/title of data are specified according to the Data Reference Syntax (https://pcmdi.llnl.gov/mips/cmip5/docs/cmip5_data_reference_syntax.pdf) as activity/product/institute/model/experiment/frequency/modeling realm/MIP table/ensemble member/version number/variable name/CMOR filename.nc.

'abrupt4xco2' is an experiment of the CMIP5 - Coupled Model Intercomparison Project Phase 5 (https://pcmdi.llnl.gov/mips/cmip5). CMIP5 is meant to provide a framework for coordinated climate change experiments for the next five years and thus includes simulations for assessment in the AR5 as well as others that extend beyond the AR5.

6.3 abrupt4xco2 (6.3 Abrupt 4XCO2) - Version 1: Impose an instantaneous quadrupling of CO2, then hold fixed.

Experiment design: https://pcmdi.llnl.gov/mips/cmip5/experiment_design.html List of output variables: https://pcmdi.llnl.gov/mips/cmip5/datadescription.html Output: time series per variable in model grid spatial resolution in netCDF format Earth System model and the simulation information: CIM repository

Entry name/title of data are specified according to the Data Reference Syntax (https://pcmdi.llnl.gov/mips/cmip5/docs/cmip5_data_reference_syntax.pdf) as activity/product/institute/model/experiment/frequency/modeling realm/MIP table/ensemble member/version number/variable name/CMOR filename.nc.

[ Derived from parent entry - See data hierarchy tab ]

'amip4xco2' is an experiment of the CMIP5 - Coupled Model Intercomparison Project Phase 5 (https://pcmdi.llnl.gov/mips/cmip5). CMIP5 is meant to provide a framework for coordinated climate change experiments for the next five years and thus includes simulations for assessment in the AR5 as well as others that extend beyond the AR5.

6.5 amip4xco2 (6.5 4xCO2 AMIP) - Version 1: Identical to expt. 6.2b, but with AMIP SSTs prescribed as in expt. 3.3 (which is the control for this run).

Experiment design: https://pcmdi.llnl.gov/mips/cmip5/experiment_design.html List of output variables: https://pcmdi.llnl.gov/mips/cmip5/datadescription.html Output: time series per variable in model grid spatial resolution in netCDF format Earth System model and the simulation information: CIM repository

Entry name/title of data are specified according to the Data Reference Syntax (https://pcmdi.llnl.gov/mips/cmip5/docs/cmip5_data_reference_syntax.pdf) as activity/product/institute/model/experiment/frequency/modeling realm/MIP table/ensemble member/version number/variable name/CMOR filename.nc.

'abrupt4xco2' is an experiment of the CMIP5 - Coupled Model Intercomparison Project Phase 5 (https://pcmdi.llnl.gov/mips/cmip5). CMIP5 is meant to provide a framework for coordinated climate change experiments for the next five years and thus includes simulations for assessment in the AR5 as well as others that extend beyond the AR5.

6.3 abrupt4xco2 (6.3 Abrupt 4XCO2) - Version 1: Impose an instantaneous quadrupling of CO2, then hold fixed.

Experiment design: https://pcmdi.llnl.gov/mips/cmip5/experiment_design.html List of output variables: https://pcmdi.llnl.gov/mips/cmip5/datadescription.html Output: time series per variable in model grid spatial resolution in netCDF format Earth System model and the simulation information: CIM repository

Entry name/title of data are specified according to the Data Reference Syntax (https://pcmdi.llnl.gov/mips/cmip5/docs/cmip5_data_reference_syntax.pdf) as activity/product/institute/model/experiment/frequency/modeling realm/MIP table/ensemble member/version number/variable name/CMOR filename.nc.