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# Coral reef state influences resilience to acute climate-mediated disturbances\_Table S1 [https://doi.org/10.5061/dryad.rfj6q57gz](https://doi.org/10.5061/dryad.rfj6q57gz) The dataset provides a summary of all publications included in the analysis for this study and the key statistics obtained from the studies and used in the analyses. The dataset includes details about the publication, spatial identifiers (e.g. realm, province, ecoregion) unique site code, information on the disturbance type and timing, the pre-and post-disturbance coral cover, the 5-year annual recovery rate, the recovery shape and recovery completeness classifications. Please see details Methods in the journal article "Coral reef state influences resilience to acute climate-mediated disturbances" as published in Global Ecology and Biogeography. ## Description of the data and file structure Each column provides the following information: | Column | Detail | | ------ | ------ | | Realm | All studies were assigned to an ‘ecoregion’, ‘province’ and ‘realm’ based on their spatial location in Spalding et al. (2007)’s spatial classification system for coastal and shelf waters. | | Province | All studies were assigned to an ‘ecoregion’, ‘province’ and ‘realm’ based on their spatial location in Spalding et al. (2007)’s spatial classification system for coastal and shelf waters. | | Ecoregion | All studies were assigned to an ‘ecoregion’, ‘province’ and ‘realm’ based on their spatial location in Spalding et al. (2007)’s spatial classification system for coastal and shelf waters. | | Unique study identifier | Unique identifiers for the lowest sampling unit in the dataset. In cases where there were data for different regions, reefs, islands/atolls, sites, reef zones, depths, and/or multiple disturbances within a publication or time-series, data from these publications were divided into separate ‘studies’. | | Publication/Dataset | Unique identifiers for the publication or dataset (generally the surname of the first author followed by the year of publication). | | Publication title | Title of the publication or dataset from which the data were sourced. | | Publication year | Year the publication from the which the data were sourced was published. | | Country/Territory | Name of the country or location from which the data came. | | Site latitude | Latitude of the study site from where the data came. | | Site longitude | Longitude of the study site from where the data came. | | Disturbance type | Classification of disturbance: Temperature stress, Cyclone/ severe storm, Runoff or Multiple. | | Disturbance.year | Year of the disturbance. | | Mean coral cover pre-disturbance | Pre-disturbance coral cover as extracted from the publication or dataset as the closest data point prior to disturbance. If there is an NA value in this column then there was no pre-disturbance data available and a measure of impact was not calculated. | | Mean coral cover post-disturbance | Post-disturbance coral cover as extracted from the publication or dataset as the closest data point prior to disturbance. If there is an NA value in this column then there was no pre-disturbance data available and a measure of impact was not calculated. | | Impact (lnRR) | Impact measure: the log response ratio of pre- to post-disturbance percentage coral cover. If there is an NA value in this column then there was no pre-disturbance data available and a measure of impact was not calculated. | | Time-averaged recovery rate | Recovery rate as percentage coral cover per year in the approximate 5-year time window following disturbance. See main Methods text in manuscript for more detail. If there is an NA value in this column then the available time-series following disturbance did not satisfy the criteria for inclusion in the calculation of recovery rate. | | Recovery shape | Recovery shape category: linear, accelerating, decelerating, logistic, flatline or null. If there is an NA value in this column then the available time-series following disturbance did not satisfy the criteria for inclusion in classification of recovery shape. | | Recovery completeness | Recovery completeness category: complete recovery – coral is observed to reach its pre-disturbance coral cover, signs of recovery – a positive trajectory but not reaching pre-disturbance cover in the time period examined, undetermined – no clear pattern in recovery, the null model was the top model, no recovery – the null model was the top model but the linear model had slope and standard error in slope near zero and further decline – the top model had a negative trend. If there is an NA value in this column then the available time-series following disturbance did not satisfy the criteria for inclusion in classification of recovery shape. | | Reference | Source for the data. | ## Sharing/Access information Data was derived from the following sources: **Appendix 1. Full list of references providing the data used in impact and recovery analyses supporting Table S1** Arceo, H. O., Quibilan, M. C., Aliño, P. M., Lim, G., & Licuanan, W. Y. (2001). Coral bleaching in Philippine reefs: Coincident evidences with mesoscale thermal anomalies. Bulletin of Marine Science, 69(2), 579-593. Aronson, R. B., Precht, W. F., Toscano, M. A., & Koltes, K. H. (2002). The 1998 bleaching event and its aftermath on a coral reef in Belize. Marine Biology, 141(3), 435-447. Aronson, R. B., Sebens, K. P., & Ebersole, J. P. (1994). Hurricane Hugo's impact on Salt River submarine canyon, St. Croix, US Virgin Islands. Proceedings of the colloquium on global aspects of coral reefs, Miami, 1993, 189-195. Bahr, K. D., Rodgers, K. S., & Jokiel, P. L. (2017). Impact of three bleaching events on the reef resiliency of Kāne'ohe Bay, Hawai'i. Frontiers in Marine Science, 4(DEC). Baird, A. H., Álvarez-Noriega, M., Cumbo, V. R., Connolly, S. R., Dornelas, M., & Madin, J. S. (2018). Effects of tropical storms on the demography of reef corals. Marine Ecology Progress Series, 606, 29-38. Barranco, L. M., Carriquiry, J. D., Rodríguez-Zaragoza, F. A., Cupul-Magaña, A. L., Villaescusa, J. A., & Calderón-Aguilera, L. E. (2016). Spatiotemporal variations of live coral cover in the Northern Mesoamerican reef system, Yucatan Peninsula, Mexico. Scientia Marina, 80(2), 143-150. Bastidas, C., Bone, D., Croquer, A., Debrot, D., Garcia, E., Humanes, A., . . . Rodríguez, S. (2012). Massive hard coral loss after a severe bleaching event in 2010 at Los Roques, Venezuela. Revista de Biologia Tropical, 60(SUPPL. 1), 29-37. Booth, D. J., & Beretta, G. A. (2002). Changes in a fish assemblage after a coral bleaching event. Marine Ecology Progress Series, 245, 205-212. Brandl, S. J., Emslie, M. J., & Ceccarelli, D. M. (2016). Habitat degradation increases functional originality in highly diverse coral reef fish assemblages. Ecosphere, 7(11). Brown, D., & Edmunds, P. J. (2013). Long-term changes in the population dynamics of the Caribbean hydrocoral Millepora spp. Journal of Experimental Marine Biology and Ecology, 441, 62-70. Brown, V. B., Davies, S. A., & Synnot, R. N. (1990). Long-term Monitoring of the Effects of Treated Sewage Effluent on the Intertidal Macroalgal Community Near Cape Schanck, Victoria, Australia. Botanica Marina, 33(1), 85-98. Bruckner, A. W., Coward, G., Bimson, K., & Rattanawongwan, T. (2017). Predation by feeding aggregations of Drupella spp. inhibits the recovery of reefs damaged by a mass bleaching event. Coral Reefs, 36(4), 1181-1187. Burt, J. 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Mass coral bleaching due to unprecedented marine heatwave in Papahānaumokuākea Marine National Monument (Northwestern Hawaiian Islands). PLoS ONE, 12(9). Crabbe, M. J. C. (2014). Evidence of initial coral community recovery at Discovery Bay on Jamaica’s north coast. Revista de Biologia Tropical, 62, 137-140. Crosbie, A. J., Bridge, T. C., Jones, G., & Baird, A. H. (2019). Response of reef corals and fish at Osprey Reef to a thermal anomaly across a 30 m depth gradient. Marine Ecology Progress Series, 622, 93-102. Darling, E. S., McClanahan, T. R., & Côté, I. M. (2010). Combined effects of two stressors on Kenyan coral reefs are additive or antagonistic, not synergistic. Conservation Letters, 3(2), 122-130. De Bakker, D. M., Meesters, E. H., Bak, R. P. M., Nieuwland, G., & Van Duyl, F. C. (2016). Long-term Shifts in Coral Communities On Shallow to Deep Reef Slopes of Curaçao and Bonaire: Are There Any Winners? Frontiers in Marine Science, 3(247). Depczynski, M., Gilmour, J. 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(2016). 27 years of benthic and coral community dynamics on turbid, highly urbanised reefs off Singapore. Scientific Reports, 6. Guillemot, N., Chabanet, P., & Le Pape, O. (2010). Cyclone effects on coral reef habitats in New Caledonia (South Pacific). Coral Reefs, 29(2), 445-453. Guzmán, H. M., & Cortés, J. (2001). Changes in reef community structure after fifteen years of natural disturbances in the Eastern Pacific (Costa Rica). Bulletin of Marine Science, 69(1), 133-149. Guzman, H. M., Cortes, J., Richmond, R. H., & Glynn, P. W. (1987). Effects of "El Nino - Southern oscillation' 1982/83 in the coral reefs at Isla del Cano, Costa Rica. Revista de Biologia Tropical, 35(2), 325-332. Haapkylä, J., Melbourne-Thomas, J., Flavell, M., & Willis, B. L. (2013). Disease outbreaks, bleaching and a cyclone drive changes in coral assemblages on an inshore reef of the Great Barrier Reef. Coral Reefs, 32(3), 815-824. Hagan, A., & Spencer, T. (2008). Reef resilience and change 1998–2007, Alphonse Atoll, Seychelles. Paper presented at the Proc 11th Int Coral Reef Symp. Harii, S., Hongo, C., Ishihara, M., Ide, Y., & Kayanne, H. (2014). Impacts of multiple disturbances on coral communities at Ishigaki Island, Okinawa, Japan, during a 15 year survey. Marine Ecology Progress Series, 509, 171-180. Harrison, H. B., Álvarez-Noriega, M., Baird, A. H., Heron, S. F., MacDonald, C., & Hughes, T. P. (2018). Back-to-back coral bleaching events on isolated atolls in the Coral Sea. Coral Reefs. Holbrook, S. J., Adam, T. C., Edmunds, P. J., Schmitt, R. J., Carpenter, R. C., Brooks, A. J., . . . Briggs, C. J. (2018). Recruitment Drives Spatial Variation in Recovery Rates of Resilient Coral Reefs. Scientific Reports, 8(1). Hongo, C., & Yamano, H. (2013). Species-Specific Responses of Corals to Bleaching Events on Anthropogenically Turbid Reefs on Okinawa Island, Japan, over a 15-year Period (1995-2009). PLoS ONE, 8(4). 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Three decades of coral reef community dynamics in St. John, USVI: A contrast of scleractinians and octocorals. Ecosphere, 8(1). Van Woesik, R., De Vantier, L. M., & Glazebrook, J. S. (1995). Effects of Cyclone "Joy' on nearshore coral communities of the Great Barrier Reef. Marine Ecology Progress Series, 128(1-3), 261-270. Van Woesik, R., Sakai, K., Ganase, A., & Loya, Y. (2011). Revisiting the winners and the losers a decade after coral bleaching. Marine Ecology Progress Series, 434, 67-76. Vercelloni, J., Kayal, M., Chancerelle, Y., & Planes, S. (2019). Exposure, vulnerability, and resiliency of French Polynesian coral reefs to environmental disturbances. Scientific Reports, 9(1). Walsh, W. J. (1983). Stability of a coral reef fish community following a catastrophic storm. Coral Reefs, 2(1), 49-63. Wilkinson, C. (2004). Status of coral reefs of the world: 2004 (Vol. 2). Queensland, Australia: Global Coral Reef Monitoring Network. Wilkinson, C. R., & Souter, D. (2008). Status of Caribbean coral reefs after bleaching and hurricanes in 2005. Wismer, S., Tebbett, S. B., Streit, R. P., & Bellwood, D. R. (2019). Spatial mismatch in fish and coral loss following 2016 mass coral bleaching. Science of the Total Environment, 650, 1487-1498. Woolsey, E., Bainbridge, S. J., Kingsford, M. J., & Byrne, M. (2012). Impacts of cyclone Hamish at One Tree Reef: Integrating environmental and benthic habitat data. Marine Biology, 159(4), 793-803. Aim: Understand the interplay between resistance and recovery on coral reefs, and investigate dependence on pre- and post-disturbance states, to inform generalisable reef resilience theory across large spatial and temporal scales. Location: Tropical coral reefs globally. Time period: 1966 to 2017. Major taxa studied: Scleratinian hard corals. Methods: We conducted a literature search to compile a global dataset of total coral cover before and after acute storms, temperature stress, and coastal runoff from flooding events. We used meta-regression to identify variables that explained significant variation in disturbance impact, including disturbance type, year, depth, and pre-disturbance coral cover. We further investigated the influence of these same variables, as well as post-disturbance coral cover and disturbance impact, on recovery rate. We examined the shape of recovery, assigning qualitatively distinct, ecologically relevant, population growth trajectories: linear, logistic, logarithmic (decelerating), and a second-order quadratic (accelerating). Results: We analysed 427 disturbance impacts and 117 recovery trajectories. Accelerating and logistic were the most common recovery shapes, underscoring non-linearities and recovery lags. A complex but meaningful relationship between the state of a reef pre- and post-disturbance, disturbance impact magnitude, and recovery rate was identified. Fastest recovery rates were predicted for intermediate to large disturbance impacts, but a decline in this rate was predicted when more than ~75% of pre-disturbance cover was lost. We identified a shifting baseline, with declines in both pre-and post-disturbance coral cover over the 50 year study period. Main conclusions: We breakdown the complexities of coral resilience, showing interplay between resistance and recovery, as well as dependence on both pre- and post-disturbance states, alongside documenting a chronic decline in these states. This has implications for predicting coral reef futures and implementing actions to enhance resilience. The dataset provides a summary of all studies included in the analysis and the key statistics obtained from the studies and used in the analyses for the manuscript entitled "Coral reef state influences resilience to acute climate-mediated disturbances" as published in Global Ecology and Biogeography. The dataset includes details about the publication, spatial identifiers (e.g. realm, province, ecoregion) unique site code, information on the disturbance type and timing, the pre-and post-disturbance coral cover, the 5-year annual recovery rate, the recovery shape and recovery completeness classifications. Please see details Methods in the journal article "Coral reef state influences resilience to acute climate-mediated disturbances" as published in Global Ecology and Biogeography.

Near continuous methane and CO2 fluxes measured along a transect on an ombrotrophic fen in Southern Sweden from August 2017-September 2019 using an automated greenhouse gas flux platform SkyLine2D. The impacts of drought (in 2018 the mire experienced drought conditions) and different vegetation types (sedge, heather, sphagnum or open water; 6 replicated for each) on the fluxes were determined. Fluxes were measured within collars of 20-cm diameter, 4-min at each collar. CH4 and CO2 fluxes were detected using a Licor infrared gas analyser (IRGA, LI-8100, Licor, NE, USA) to measure CO2 and a cavity ringdown laser (CRD, LGR U-GGA-91, Los Gatos Research, CA USA) to measure both CO2 and CH4. Fluxes of CO2 and CH4 were calculated using linear regression; a deadband of at least 20 seconds was allowed for the chamber headspace to mix and a window of 90 seconds was used for CO2 and 240 seconds used for CH4. Fluxes were adjusted for area, air temperature and gas volume. Further adjustment was made to the CO2 fluxes during daylight hours based upon the light response curve to account for attenuation of light by the chamber material, after. All data manipulation and analyses were carried out using SAS 9.4 (SAS Institute, CA 161 USA). GHG flux data (for both CO2 and CH4) were quality controlled in the first instance using the R2 statistic of the CO2 flux measurement, with values < 0.9 discarded. Measurements passing this threshold were then assessed using the output statistics from the regression calculation of CH4 fluxes, where regressions with a P value < 0.05 were accepted, while those that did not were treated as zero flux. Data outliers were defined as those ± 1.96 standard errors of the mean flux value for each collar and were excluded from the analyses. Data were further filtered to account for overestimation of fluxes during still atmospheric night-time conditions. Using the procedure fluxes where the mean CO2 concentration for the 20 second period before and after chamber closure dropped by more than 25 ppm where discounted. Net ecosystem exchange and methane fluxes were measured from a hemi-boreal ombrotrophic fen in Southern Sweden. An automated chamber system, SkyLine2D, was used to measure the fluxes near-continuously from August 2017 to September 2019. Four ecotypes were identified: sphagnum (Sphagnum spp), eriophorum, heather and water, to assess how these different ecotypes would respond to drought. The 2018 drought allowed comparison of fluxes between drought and non-drought years (May to September), and their recovery the following year.

This file contains the data and the STATA estimation code to replicate the results in the article entitled: "Information Communication Technology development and energy demand in African countries".

This data release contains the boundaries of assessment units and input data for the assessment of undiscovered oil and gas resources in the Mowry formation of the Wind River Basin Province in Wyoming. The Assessment Unit is the fundamental unit used in the National Assessment Project for the assessment of undiscovered oil and gas resources. The Assessment Unit is defined within the context of the higher-level Total Petroleum System. The Assessment Unit is shown herein as a geographic boundary interpreted, defined, and mapped by the geologist responsible for the province and incorporates a set of known or postulated oil and (or) gas accumulations sharing similar geologic, geographic, and temporal properties within the Total Petroleum System, such as source rock, timing, migration pathways, trapping mechanism, and hydrocarbon type. The Assessment Unit boundary is defined geologically as the limits of the geologic elements that define the Assessment Unit, such as limits of reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are Assessment Units that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the Assessment Unit boundary. Methodology of assessments are documented in USGS Data Series 547 for continuous assessments (https://pubs.usgs.gov/ds/547) and USGS DDS69-D, Chapter 21 for conventional assessments (https://pubs.usgs.gov/dds/dds-069/dds-069-d/REPORTS/69_D_CH_21.pdf). See supplemental information for a detailed list of files included this data release.

The study examines the linear and nonlinear relationships between per capita carbon dioxide emission, per capita real GDP, energy consumption, financial development, foreign direct investment, trade openness, urbanization, agriculture, and industry sectors as potential determining factors of CO2 emissions in the perspective of Bangladesh all through 44 years, starting from 1974. The study considers the CO2 emissions from the selected South Asian countries over the period from 1978 and 2018. The study uses three cointegration approaches. First, we employ linear cointegration method and find that crucial determining factors of CO2 emissions in Bangladesh are real GDP per capita, energy consumption, and urbanization. Then, we apply the nonlinear cointegration method and find that energy consumption and FDI have asymmetric impacts on carbon release in the long run. While energy consumption, financial development, and FDI have asymmetric influence in the short run. Finally, we apply a panel cointegration test to compare Bangladesh with other South Asian countries in terms of CO2 emissions. The estimated results disclose that the vital contributing factors of CO2 emissions in selected South Asian countries are real GDP, energy consumption, financial development, and urbanization. Our results show that energy consumption, financial development, and urbanization upturn CO2 emissions, while trade openness lowers emissions. We also claim that our results are consistent with the EKC hypothesis for both in Bangladesh and selected South Asian countries. The three cointegration estimation findings disclose that urbanization will deteriorate environmental worth in Bangladesh and selected South Asian countries in the long run.

Weather stations deployed across the Critical Zone Observatory (CZO) Shale Transect, including sites in New York, Virginia, Tennessee, Alabama and Puerto Rico, provide continuous measurements of climatic conditions influencing shale weathering. Measurements are recorded every two hours and include precipitation, air temperature, relative humidity, solar radiation, wind speed, soil temperature, soil moisture and soil electrical conductivity. Ashlee Dere, (2014), Rates and mechanisms of shale weathering across a latitudinal climosequence, Ph.D. Dissertation, The Pennsylvania State University

The Mara River basin in East Africa is a trans-boundary river basin that highlights many of the development and conservation challenges in East Africa. The Mara River flows from its headwaters in the Mau Forest of Kenya through the northern portion of the Serengeti-Mara Ecosystem and into Lake Victoria in Tanzania, where it forms part of the headwaters of the Nile River basin. Changes in land use and landcover in the basin have raised concerns about the quality and quantity of water in the Mara River. We analyzed sediment cores from the Mara Wetland (near the river’s outlet into Lake Victoria) to evaluate how sedimentation rates and sources have changed historically, through a period marked by major changes in human and livestock population densities, land use, and disease epidemics (rinderpest). We collected sediment cores in August 2015 along a transect through the Mara Wetland from the upstream reaches to Lake Victoria. Slices of sediment cores collected from four distinct regions of the wetland were age-calibrated using radiocarbon and lead-210 dating methods. Core slices were then analyzed for sediment sources using a sediment fingerprinting approach, nitrogen and carbon stable isotope signatures, and mercury. Our results suggest that ecological conditions in the Mara River basin were fairly stable over paleoecological time scales (2000-1000 years before present), but there has been a period of rapid change in the basin over the last 250 years, particularly since the 1960s, likely due to anthropogenic factors. We also observed that downstream effects of landcover and land use change can be exacerbated by increasing occurrence of extreme rainfall events in the region. The Mara Wetland likely plays an important role in mitigating the impact of those factors on Lake Victoria. This work was the result of a partnership between Yale University, the Cary Institute of Ecosystem Studies, WWF-UK, WWF-Kenya and WWF-Tanzania.

This data collection is part of a longitudinal survey designed to provide detailed information on the economic situation of households and persons in the United States. These data examine the distribution of income, wealth, and poverty in American society and gauge the effects of federal and state programs on the well-being of families and individuals. There are three basic elements contained in the survey. The first is a control card that records basic social and demographic characteristics for each person in a household, as well as changes in such characteristics over the course of the interviewing period. The second element is the core portion of the questionnaire, with questions repeated at each interview on labor force activity, types and amounts of income, participation in various cash and noncash benefit programs, attendance in post-secondary schools, private health insurance coverage, public or subsidized rental housing, low-income energy assistance, and school breakfast and lunch participation. The third element consists of topical modules, which are a series of supplemental questions asked during selected household visits. Topical modules include some core data to help link individuals to the core files. A topical module was not created for Wave I. The Wave II Topical Module (Part 17) covers recipiency, employment, work disability, education and training, marital status, migration, and fertility histories along with household relationships. The Wave III Topical Module (Part 19) includes data on work schedules, child care, child support agreements, support for nonhousehold members, functional limitations and disability, and utilization of health care services. Data from the Wave IV Topical Module (Part 21) include assets and liabilities, retirement expectations and pension plan coverage, and real estate property and vehicles. The Wave V Topical Module (Part 23) provides data on educational financing and enrollment. The Wave VI Topical Module (Part 25) covers time spent outside the work force, child care, child support agreements, support for nonhousehold members, functional limitations and disability, and utilization of health care services. Data in the Wave VII Topical Module (Part 27) cover selected financial assets, medical expenses and work disability, and real estate, shelter costs, dependent care, and vehicles. Wave VIII Topical Module (Part 29) includes data on annual income and retirement accounts, taxes, and school enrollment and financing. Part 33 of this study is the Wave V Topical Module Research File, an unedited version of Part 23. This research file has not been edited nor imputed but has been topcoded or bottomcoded and recoded if necessary by the Census Bureau to avoid disclosure of individual respondents' identities. Datasets: DS0: Study-Level Files DS1: Wave I Rectangular Data DS2: Data Dictionary for Wave I Rectangular File DS3: Wave II Rectangular Data DS4: Data Dictionary for Wave II Rectangular File DS5: Wave III Rectangular Data DS6: Data Dictionary for Wave III Rectangular File DS7: Wave IV Core Microdata File DS8: Data Dictionary for Wave IV Core Microdata File DS9: Wave V Core Microdata File DS10: Data Dictionary for Wave V Core Microdata File DS11: Wave VI Core Microdata File DS12: Data Dictionary for Wave VI Core Microdata File DS13: Wave VII Core Microdata File DS14: Data Dictionary for Wave VII Core Microdata File DS15: Wave VIII Core Microdata File DS16: Data Dictionary for Wave VIII Core Microdata File DS17: Wave II Topical Module Microdata File DS18: Data Dictionary for Wave II Topical Module Microdata File DS19: Wave III Topical Module Microdata File DS20: Data Dictionary for Wave III Topical Module Microdata File DS21: Wave IV Topical Module Microdata File DS22: Data Dictionary for Wave IV Topical Module Microdata File DS23: Wave V Topical Module Microdata File DS24: Data Dictionary for Wave V Topical Module Microdata File DS25: Wave VI Topical Module Microdata File DS26: Data Dictionary for Wave VI Topical Module Microdata File DS27: Wave VII Topical Module Microdata File DS28: Data Dictionary for Wave VII Topical Module Microdata File DS29: Wave VIII Topical Module Microdata File DS30: Data Dictionary for Wave VIII Topical Module Microdata File DS31: User Notes DS32: User Guide DS33: Wave V Topical Module Research File A multistage stratified sampling design was used. One-fourth of the sample households were interviewed each month, and households were reinterviewed at four-month intervals. All persons at least 15 years old who were present as household members at the time of the first interview were included for the entire study, except those who joined the military, were institutionalized for the entire study period, or moved from the United States. Original household members who moved during the study period were followed to their new residences and interviewed there. New persons moving into households of members of the original sample also were included in the survey, but were not followed if they left the household of an original sample person. Beginning with the 1990 Panel, the file structure of SIPP has changed. The unit of observation is one record for each person for each month, rather than one record per person. Also, topical modules are provided separately from core files.The codebooks are provided by ICPSR as a Portable Document Format (PDF) files and the data dictionaries are provided as ASCII text files. The PDF file format was developed by Adobe Systems Incorporated and can be accessed using PDF reader software, such as the Adobe Acrobat Reader. Information on how to obtain a copy of the Acrobat Reader is provided on the ICPSR Web site. Resident population of the United States, excluding persons living in institutions and military barracks.

In refrigeration and power cycles processes the use of air as heatsink for condensing, when large quantities of water are not available, is a common practice and even more frequent. Although ambient air has the great advantage of being available everywhere and in unlimited quantity, it also has numerous disadvantages: highly variable temperatures, low exchange coefficients, need to use of large heat exchange surfaces and large air quantities. To limit these drawbacks, LU-VE has developed an innovative solution named EMERITUS. This technology has a fan-cooled exchanger on which two additional water cooling systems are used in sequence: treated water is sprayed onto the heat exchanger coil and the remaining non-evaporated is collected and used on to the adiabatic panel. This combination of the two techniques has positive effects on both the thermal capacity exchanged and the quantity of water consumed. The article describes the functional principle of the new EMERITUS and analyses a case study in which the running costs of a water chiller combined with EMERITUS are compared with other traditional solutions.