analyze the health and retirement study (hrs) with r the hrs is the one and only longitudinal survey of american seniors. with a panel starting its third decade, the current pool of respondents includes older folks who have been interviewed every two years as far back as 1992. unlike cross-sectional or shorter panel surveys, respondents keep responding until, well, death d o us part. paid for by the national institute on aging and administered by the university of michigan's institute for social research, if you apply for an interviewer job with them, i hope you like werther's original. figuring out how to analyze this data set might trigger your fight-or-flight synapses if you just start clicking arou nd on michigan's website. instead, read pages numbered 10-17 (pdf pages 12-19) of this introduction pdf and don't touch the data until you understand figure a-3 on that last page. if you start enjoying yourself, here's the whole book. after that, it's time to register for access to the (free) data. keep your username and password handy, you'll need it for the top of the download automation r script. next, look at this data flowchart to get an idea of why the data download page is such a righteous jungle. but wait, good news: umich recently farmed out its data management to the rand corporation, who promptly constructed a giant consolidated file with one record per respondent across the whole panel. oh so beautiful. the rand hrs files make much of the older data and syntax examples obsolete, so when you come across stuff like instructions on how to merge years, you can happily ignore them - rand has done it for you. the health and retirement study only includes noninstitutionalized adults when new respondents get added to the panel (as they were in 1992, 1993, 1998, 2004, and 2010) but once they're in, they're in - respondents have a weight of zero for interview waves when they were nursing home residents; but they're still responding and will continue to contribute to your statistics so long as you're generalizing about a population from a previous wave (for example: it's possible to compute "among all americans who were 50+ years old in 1998, x% lived in nursing homes by 2010"). my source for that 411? page 13 of the design doc. wicked. this new github repository contains five scripts: 1992 - 2010 download HRS microdata.R loop through every year and every file, download, then unzip everything in one big party impor t longitudinal RAND contributed files.R create a SQLite database (.db) on the local disk load the rand, rand-cams, and both rand-family files into the database (.db) in chunks (to prevent overloading ram) longitudinal RAND - analysis examples.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create tw o database-backed complex sample survey object, using a taylor-series linearization design perform a mountain of analysis examples with wave weights from two different points in the panel import example HRS file.R load a fixed-width file using only the sas importation script directly into ram with < a href="http://blog.revolutionanalytics.com/2012/07/importing-public-data-with-sas-instructions-into-r.html">SAScii parse through the IF block at the bottom of the sas importation script, blank out a number of variables save the file as an R data file (.rda) for fast loading later replicate 2002 regression.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create a database-backed complex sample survey object, using a taylor-series linearization design exactly match the final regression shown in this document provided by analysts at RAND as an update of the regression on pdf page B76 of this document . click here to view these five scripts for more detail about the health and retirement study (hrs), visit: michigan's hrs homepage rand's hrs homepage the hrs wikipedia page a running list of publications using hrs notes: exemplary work making it this far. as a reward, here's the detailed codebook for the main rand hrs file. note that rand also creates 'flat files' for every survey wave, but really, most every analysis you c an think of is possible using just the four files imported with the rand importation script above. if you must work with the non-rand files, there's an example of how to import a single hrs (umich-created) file, but if you wish to import more than one, you'll have to write some for loops yourself. confidential to sas, spss, stata, and sudaan users: a tidal wave is coming. you can get water up your nose and be dragged out to sea, or you can grab a surf board. time to transition to r. :D

The University of Michigan Health and Retirement Study (HRS) is a longitudinal panel study that surveys a representative sample of approximately 20,000 Americans over 50 years old. Core surveys take place every two years, with additional topics covered in off-year surveys. Data collected through a mix of phone and face-to-face interviews provides a comprehensive look at the changing experiences of older Americans on a range of topics including physical and mental health history and status, cognition, family structure, health care utilization and costs, financial status, and employment history and retirement plans. Interviews are supplemented with information from physical measurements, biomarker and genetic data, and linkages to administrative data.

The dataset includes information on lifestyle (smoking, alcohol consumption, height, weight (at age 20 and time of data collection), previous night work and diurnal preference. A brief questionnaire was send to all employees from three of the five Danish regions. Hereof, 37 077 employees (56.2%) responded. Data can be linked at individual level to information in Danish Working Hour Database - Working Hours (doi:10.7910/DVN/GUFZDI)

PurposeRecent studies report systematic differences in how individuals categorize the severity of identical health and work limitation vignettes. We investigate how health professionals and disability recipients characterize the severity of work limitations and whether their reporting patterns are robust to demographic, education, and health characteristics. We use the results to illustrate the potential impact of reporting heterogeneity on the distribution of work disability estimated from self-reported categorical health and disability data.MethodNationally representative data on anchoring disability vignettes from the 2004 Health and Retirement Study (HRS) are used to investigate how respondents with an occupation background in health and Social Security disability beneficiaries categorize work limitation vignettes. Using pain, cardiovascular health, and depression vignettes, we estimate generalized ordered probit models (N = 2,660 individuals or 39,681 person-vignette observations) that allow the severity thresholds to vary by respondent characteristics.ResultsWe find that health professionals (excluding nurses) and disability recipients tend to classify identical work limitations as more severe compared to non-health professional non-disabled respondents. For disability recipients, the differences are most pronounced and particularly visible in the tails of the work limitations distribution. For health professionals, we observe smaller differences, affecting primarily the classification of mildly and moderately severe work limitations. The patterns for health professionals (excluding nurses) are robust to demographics, education, and health conditions. The greater likelihood of viewing the vignette person as more severely work limited observed among disability recipients is mostly explained by the fact that these respondents also tend to be in poorer health which itself predicts a more inclusive scale.ConclusionsKnowledge of reporting scales from health professionals and disabled individuals can benefit researchers in a broad range of applications in health and disability research. They may be useful as reference scales to evaluate disability survey data. Such knowledge may be beneficial when studying disability programs. Given the increasing availability of anchoring vignette data in surveys, this is a promising area for future evaluation research.

Protein-Protein, Genetic, and Chemical Interactions for Hanyaloglu AC (2005):Essential role of Hrs in a recycling mechanism mediating functional resensitization of cell signaling. curated by BioGRID (https://thebiogrid.org); ABSTRACT: Hepatocyte growth factor-regulated tyrosine kinase substrate (Hrs) is well known to terminate cell signaling by sorting activated receptors to the MVB/lysosomal pathway. Here we identify a distinct role of Hrs in promoting rapid recycling of endocytosed signaling receptors to the plasma membrane. This function of Hrs is specific for receptors that recycle in a sequence-directed manner, in contrast to default recycling by bulk membrane flow, and is distinguishable in several ways from previously identified membrane-trafficking functions of Hrs/Vps27p. In particular, Hrs function in sequence-directed recycling does not require other mammalian Class E gene products involved in MVB/lysosomal sorting, nor is receptor ubiquitination required. Mutational studies suggest that the VHS domain of Hrs plays an important role in sequence-directed recycling. Disrupting Hrs-dependent recycling prevented functional resensitization of the beta(2)-adrenergic receptor, converting the temporal profile of cell signaling by this prototypic G protein-coupled receptor from sustained to transient. These studies identify a novel function of Hrs in a cargo-specific recycling mechanism, which is critical to controlling functional activity of the largest known family of signaling receptors.

This dataset contains the World Average Degree Days Database for the period 1964-2013. Follow datasource.kapsarc.org for timely data to advance energy economics research.*

Summary_64-13_freq=1D Average Degree Days of various indices for respective countries for the period 1964-2013, converted to a 1 day frequency

Summary_64-13_freq=6hrs Average Degree Days of various indices for respective countries for the period 1964-2013, calculated at 6 hrs frequency

T2m.hdd.18C Calculation of Heating Degree Days using plain temperature at 2 m elevation at Tref=18°C and frequency of 6 hrs

T2m.cdd.18C Calculation of Cooling Degree Days using plain temperature at 2 m elevation at Tref=18°C and frequency of 6 hrs

t2m.hdd.15.6C Calculation of Heating Degree Days using plain temperature at 2 m elevation at Tref=15.6°C and frequency of 6 hrs

t2m.hdd.18.3C Calculation of Heating Degree Days using plain temperature at 2 m elevation at Tref=18.3°C and frequency of 6 hrs

t2m.hdd.21.1C Calculation of Heating Degree Days using plain temperature at 2 m elevation at Tref=21.1°C and frequency of 6 hrs

t2m.cdd.15.6C Calculation of Cooling Degree Days using plain temperature at 2 m elevation at Tref=15.6°C and frequency of 6 hrs

t2m.cdd.18.3C Calculation of Cooling Degree Days using plain temperature at 2 m elevation at Tref=18.3°C and frequency of 6 hrs

t2m.cdd.21.1C Calculation of Cooling Degree Days using plain temperature at 2 m elevation at Tref=21.1°C and frequency of 6 hrs

t2m.hdd.60F Calculation of Heating Degree Days using plain temperature at 2 m elevation at Tref=60°F and frequency of 6 hrs

t2m.hdd.65F Calculation of Heating Degree Days using plain temperature at 2 m elevation at Tref=65°F and frequency of 6 hrs

t2m.hdd.70F Calculation of Heating Degree Days using plain temperature at 2 m elevation at Tref=70°F and frequency of 6 hrs

t2m.cdd.60F Calculation of Cooling Degree Days using plain temperature at 2 m elevation at Tref=60°F and frequency of 6 hrs

t2m.cdd.65F Calculation of Cooling Degree Days using plain temperature at 2 m elevation at Tref=65°F and frequency of 6 hrs

t2m.cdd.70F Calculation of Cooling Degree Days using plain temperature at 2 m elevation at Tref=70°F and frequency of 6 hrs

HI.hdd.57.56F Calculation of Heating Degree Days using the Heat Index at Tref=57.56°F and frequency of 6 hrs

HI.hdd.63.08F Calculation of Heating Degree Days using the Heat Index at Tref=63.08°F and frequency of 6 hrs

HI.hdd.68.58F Calculation of Heating Degree Days using the Heat Index at Tref=68.58°F and frequency of 6 hrs

HI.cdd.57.56F Calculation of Cooling Degree Days using the Heat Index at Tref=57.56°F and frequency of 6 hrs

HI.cdd.63.08F Calculation of Cooling Degree Days using the Heat Index at Tref=63.08°F and frequency of 6 hrs

HI.cdd.68.58F Calculation of Cooling Degree Days using the Heat Index at Tref=68.58°F and frequency of 6 hrs

HUM.hdd.13.98C Calculation of Heating Degree Days using the Humidex at Tref=13.98°C and frequency of 6 hrs

HUM.hdd.17.4C Calculation of Heating Degree Days using the Humidex at Tref=17.40°C and frequency of 6 hrs

HUM.hdd.21.09C Calculation of Heating Degree Days using the Humidex at Tref=21.09°C and frequency of 6 hrs

HUM.cdd.13.98C Calculation of Cooling Degree Days using the Humidex at Tref=13.98°C and frequency of 6 hrs

HUM.cdd.17.4C Calculation of Cooling Degree Days using the Humidex at Tref=17.40°C and frequency of 6 hrs

HUM.cdd.21.09C Calculation of Cooling Degree Days using the Humidex at Tref=21.09°C and frequency of 6 hrs

ESI.hdd.12.6C Calculation of Heating Degree Days using the Environmental Stress Index at Tref=12.6°C and frequency of 6 hrs

ESI.hdd.14.9C Calculation of Heating Degree Days using the Environmental Stress Index at Tref=14.9°C and frequency of 6 hrs

ESI.hdd.17.2C Calculation of Heating Degree Days using the Environmental Stress Index at Tref=17.2°C and frequency of 6 hrs

ESI.cdd.12.6C Calculation of Cooling Degree Days using the Environmental Stress Index at Tref=12.6°C and frequency of 6 hrs

ESI.cdd.14.9C Calculation of Cooling Degree Days using the Environmental Stress Index at Tref=14.9°C and frequency of 6 hrs

ESI.cdd.17.2C Calculation of Cooling Degree Days using the Environmental Stress Index at Tref=17.2°C and frequency of 6 hrs

Note:

Divide Degree Days by 4 to convert from 6 hrs to daily frequency

Protein-Protein, Genetic, and Chemical Interactions for Hasseine LK (2007):Hrs is a positive regulator of VEGF and insulin signaling. curated by BioGRID (https://thebiogrid.org); ABSTRACT: Both VEGF and insulin are implicated in the pathogenesis of diabetic retinopathy. While it has been established for many years that the number of cell surface receptors impacts upon VEGF and insulin action, little is known about the precise machinery and proteins driving VEGF-R2 and IR degradation. Here, we investigate the role of Hepatocyte growth factor-Regulated tyrosine kinase Substrate (Hrs), a regulator of RTK trafficking, in VEGF and insulin signaling. We report that ectopic expression of Hrs increases VEGF-R2 and IR number and tyrosine phosphorylation, leading to amplification of their downstream signaling. The UIM (Ubiquitin Interacting Motif) domain of Hrs is required for Hrs-induced increases in VEGF-R2, but not in IR. Furthermore, Hrs is tyrosine-phosphorylated in response to VEGF and insulin. We show that the UIM domain is required for Hrs phosphorylation in response to VEGF, but not to insulin. Importantly, Hrs co-localizes with both VEGF-R2 and IR and co-immunoprecipitates with both in a manner independent of the Hrs-UIM domain. Finally, we demonstrate that Hrs inhibits Nedd4-mediated VEGF-R2 degradation and acts additively with Grb10. We conclude that Hrs is a positive regulator of VEGF-R2 and IR signaling and that ectopic expression of Hrs protects both VEGF-R2 and IR from degradation.

Protein-Protein, Genetic, and Chemical Interactions for Kobayashi H (2005):Hrs, a mammalian master molecule in vesicular transport and protein sorting, suppresses the degradation of ESCRT proteins signal transducing adaptor molecule 1 and 2. curated by BioGRID (https://thebiogrid.org); ABSTRACT: The degradation and sorting of cytoplasmic and cell-surface proteins are crucial steps in the control of cellular functions. We previously identified three mammalian Vps (vacuolar protein sorting) proteins, Hrs (hepatocyte growth factor-regulated tyrosine kinase substrate) and signal transducing adaptor molecule (STAM) 1 and -2, which are tyrosine-phosphorylated upon cytokine/growth factor stimulation. Hrs and the STAMs each contain a ubiquitin-interacting motif and through formation of a complex are involved in the vesicle transport of early endosomes. To explore the mechanism and cellular function of this complex in mammalian cells, we established an Hrs-defective fibroblastoid cell line (hrs(-/-)); embryos with this genotype died in utero. In the hrs(-/-) cells only trace amounts of STAM1 and STAM2 were detected. Introduction of wild-type Hrs or an Hrs mutant with an intact STAM binding domain (Hrs-dFYVE) fully restored STAM1 and STAM2 expression, whereas mutants with no STAM binding ability (Hrs-dC2, Hrs-dM) failed to express the STAMs. This regulated control of STAM expression by Hrs was independent of transcription. Interestingly, STAM1 degradation was mediated by proteasomes and was partially dependent on the ubiquitin-interacting motif of STAM1. Revertant Hrs expression in hrs(-/-) cells not only led to the accumulation of ubiquitinated proteins, including intracytoplasmic vesicles, but also restored STAM1 levels in early endosomes and eliminated the enlarged endosome phenotype caused by the absence of Hrs. These results suggest that Hrs is a master molecule that controls in part the degradation of STAM1 and the accumulation of ubiquitinated proteins.

In the study: ‘Environmental drivers of global variation in home range size of terrestrial and marine mammals’, the relationship between home range size (HRS) and environmental variables was quantified, accounting for species traits and their interactions with environmental variables for terrestrial and marine mammals. A novel, comprehensive dataset of 2,800 HRS estimates from 586 terrestrial and 27 marine mammal was used. The results indicated that terrestrial mammal HRS was on average 5.3 times larger in areas with low human disturbance (human footprint index [HFI] = 0), compared to areas with maximum human disturbance (HFI = 50). Similarly, HRS was on average 5.4 times larger in areas with low annual mean productivity (NDVI = 0), compared to areas with high productivity (NDVI = 1). In addition, HRS increased by a factor of 1.9 on average from low to high seasonality in productivity (standard deviation (SD) of monthly NDVI from 0 to 0.36). Of these environmental variables, human disturbance and annual mean productivity explained a larger proportion of HRS variance than seasonality in productivity. Marine mammal HRS decreased, on average, by a factor of 3.7 per 10 °C decline in annual mean sea surface temperature (SST), and increased by a factor of 1.5 per 1 °C increase in SST seasonality (SD of monthly values). Annual mean SST explained more variance in HRS than SST seasonality. In this repository the following data were published that were used to perform the analyses: - Home_range_data: Excelsheet with the subset of home range sizes from the HomeRange database (Broekman et al., 2023) included in the analysis with associated data on the values of species traits and environmental variables. - Home_range_metadata: description of the sheets and columns in the Home_range_data file. - Data_sources: List of references for the home range data included in this study.

United States Avg Weekly Hours: PW: Information data was reported at 35.100 Hour in Oct 2018. This records a decrease from the previous number of 36.200 Hour for Sep 2018. United States Avg Weekly Hours: PW: Information data is updated monthly, averaging 36.400 Hour from Jan 1964 (Median) to Oct 2018, with 658 observations. The data reached an all-time high of 38.600 Hour in Aug 1966 and a record low of 35.000 Hour in Jan 1982. United States Avg Weekly Hours: PW: Information data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G039: Current Employment Statistics Survey: Average Weekly Hours: Production Workers.

Finland Hours Worked: Human Health & Social Work Activities (HA) data was reported at 163,816.000 Hour th in Jun 2018. This records a decrease from the previous number of 167,425.000 Hour th for Mar 2018. Finland Hours Worked: Human Health & Social Work Activities (HA) data is updated quarterly, averaging 149,262.000 Hour th from Mar 2005 (Median) to Jun 2018, with 54 observations. The data reached an all-time high of 167,425.000 Hour th in Mar 2018 and a record low of 126,619.000 Hour th in Sep 2006. Finland Hours Worked: Human Health & Social Work Activities (HA) data remains active status in CEIC and is reported by Statistics Finland. The data is categorized under Global Database’s Finland – Table FI.G027: Hours Worked: by Industry.

Protein-Protein, Genetic, and Chemical Interactions for Belleudi F (2009):Hrs regulates the endocytic sorting of the fibroblast growth factor receptor 2b. curated by BioGRID (https://thebiogrid.org); ABSTRACT: The keratinocyte growth factor receptor or fibroblast growth factor receptor 2b (KGFR/FGFR2b) is activated by the specific interaction with the keratinocyte growth factor (KGF/FGF7), which targets the receptor to the degradative pathway, and the fibroblast growth factor 10 (FGF10/KGF2), which drives the receptor to the juxtanuclear recycling route. Hrs plays a key role in the regulation of the endocytic degradative transport of ubiquitinated receptor tyrosine kinases, but the direct involvement of this protein in the regulation of FGFR endocytosis has not been investigated yet. We investigated here the possible role of Hrs in the alternative endocytic pathways of KGFR. Quantitative immunofluorescence microscopy and biochemical analysis showed that both overexpression and siRNA interference of Hrs inhibit the KGF-triggered KGFR degradation, blocking receptor transport to lysosomes and causing its rapid reappearance at the plasma membrane. In contrast, the FGF10-induced KGFR targeting to the recycling compartment is not affected by Hrs overexpression or depletion. Coimmunoprecipitation approaches indicated that Hrs is recruited to KGFR only after KGF treatment, although it is not tyrosine phosphorylated by the ligand. In conclusion, Hrs regulates the KGFR degradative pathway, but not its juxtanuclear recycling transport. In addition, the results suggest that Hrs recruitment to the receptor, but not its ligand-induced phosphorylation, could be required for its function.

Protein-Protein, Genetic, and Chemical Interactions for Hu J (2007):STAM and Hrs down-regulate ciliary TRP receptors. curated by BioGRID (https://thebiogrid.org); ABSTRACT: Cilia are endowed with membrane receptors, channels, and signaling components whose localization and function must be tightly controlled. In primary cilia of mammalian kidney epithelia and sensory cilia of Caenorhabditis elegans neurons, polycystin-1 (PC1) and transient receptor polycystin-2 channel (TRPP2 or PC2), function together as a mechanosensory receptor-channel complex. Despite the importance of the polycystins in sensory transduction, the mechanisms that regulate polycystin activity and localization, or ciliary membrane receptors in general, remain poorly understood. We demonstrate that signal transduction adaptor molecule STAM-1A interacts with C. elegans LOV-1 (PC1), and that STAM functions with hepatocyte growth factor-regulated tyrosine kinase substrate (Hrs) on early endosomes to direct the LOV-1-PKD-2 complex for lysosomal degradation. In a stam-1 mutant, both LOV-1 and PKD-2 improperly accumulate at the ciliary base. Conversely, overexpression of STAM or Hrs promotes the removal of PKD-2 from cilia, culminating in sensory behavioral defects. These data reveal that the STAM-Hrs complex, which down-regulates ligand-activated growth factor receptors from the cell surface of yeast and mammalian cells, also regulates the localization and signaling of a ciliary PC1 receptor-TRPP2 complex.

Avg Weekly Hours: Mfg: Non Durable data was reported at 40.100 Hour in Jun 2018. This stayed constant from the previous number of 40.100 Hour for May 2018. Avg Weekly Hours: Mfg: Non Durable data is updated monthly, averaging 39.900 Hour from Mar 2006 (Median) to Jun 2018, with 148 observations. The data reached an all-time high of 40.800 Hour in Dec 2012 and a record low of 38.100 Hour in Apr 2009. Avg Weekly Hours: Mfg: Non Durable data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G037: Current Employment Statistics Survey: Average Weekly Hours.

"Sunshine is important for our health and recreation, and for the environment. It is also important for our agriculture-based economy, for example, for plant growth.

This layer shows annual sunshine hours across New Zealand for 2000 as part of the data series for years 1972 to 2013. Data is for a calendar year (January-December).

The National Institute of Water and Atmospheric Research (NIWA) mapped mean annual sunshine hours from the virtual climate station network data (NIWA) generated from data in its National Climate Database, for the period 1981–2013. It generated the Units: percentage of normal by comparing the annual average to the long-term mean for 1981–2010. Maps were produced using the Virtual Climate Station network data. Data for each year are measured over the calendar year (January–December).

The accuracy of the data source is of high quality.

This dataset relates to the "Sunshine hours in New Zealand" measure on the Environmental Indicators, Te taiao Aotearoa website.

Geometry: grid Unit: hrs/yr"

Increase the number of service hours provided in the Corporation for National and Community Services Volunteer Programs (CNCS) from 1,654,783 in 2014 to 2,150,434 by 2017.

Mexico Employment Rate: ENOE 2010: Hrs Work per Week: More Than 48 Hrs data was reported at 28.821 % in Dec 2018. This records an increase from the previous number of 28.351 % for Sep 2018. Mexico Employment Rate: ENOE 2010: Hrs Work per Week: More Than 48 Hrs data is updated quarterly, averaging 28.329 % from Mar 2005 (Median) to Dec 2018, with 56 observations. The data reached an all-time high of 30.165 % in Dec 2005 and a record low of 26.826 % in Jun 2014. Mexico Employment Rate: ENOE 2010: Hrs Work per Week: More Than 48 Hrs data remains active status in CEIC and is reported by National Institute of Statistics and Geography. The data is categorized under Global Database’s Mexico – Table MX.G019: Employment: ENOE 2010: Age 15 and Above.