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How do I find U.S. Census data?
Visit census.gov (http://census.gov/) to browse quality information current and historical facts and figures about Americas people, places, and economy. An additional tool offered by the U.S. Census Bureau, the data.census.gov (https://data.census.gov/) is a platform designed to help users access demographic and economic data digitally. The Census Academy (https://www.census.gov/data/academy.html) has many short tutorials for searching this website. For more information, consult the Census Bureau's FAQ (https://ask.census.gov/) , or schedule an appointment with an LSU Libraries Librarian here (https://lsu.libcal.com/appointments/caple) . The census on microfilm LSU owns is limited. The only states in this collection include: Alabama, Arkansas, District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, Missouri, Pennsylvania, South Carolina, Tennessee, Texas, Virginia (and scattered census material for West Virginia). Information on other states may be located at the National Archives (http://www.archives.gov/research/start/index.html) in Washington D.C., the regional branches (http://www.archives.gov/locations/index.html) of the National Archives, as well as the Bluebonnet Regional Branch of the East Baton Rouge Parish Library (https://www.ebrpl.com/) . The collection of census material at LSU Libraries includes population schedules, agricultural census data, lists of manufactures, slave schedules, passenger lists for the port of New Orleans covering 1853-1899, social statistics, and scattered information concerning Defective, Dependent and Delinquent Classes. Other material that may be helpful for researching archives for genealogy information include Records of the Diocese of Louisiana and the "Floridas", New Orleans City Directories for years 1805-1945, New Orleans Christian Advocate concerning Marriage and Death Notices, Military Academy Letters, and Indian Affairs, just to name a few. If you would like to access any of these materials, contact libgovdocs@lsu.edu . Answered by: Kendall Caple

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2058653
Enhancing imputation accuracy for catch-all missing data mechanisms with DFBETAS and leverage
This paper addresses the challenge of missing data in scientific research. It specifically examines the case of missing data arising from a “catch-all” missing not at ran (MNAR) mechanism, where missing values are disproportionately from one category, such as income or ethnicity in surveys. The study introduces the use of the regression diagnostic DFBETAS along with Leverage to improve the imputation of categorical data under such conditions. DFBETAS, a measure of influence in regression, is adapted to capture the intrinsic information of missing values, thereby enhancing the imputation process within a Bayesian multiple imputation (MI) framework. We validate the proposed approach through Monte Carlo simulations with data generating mechanisms based on probability distributions. The results show that incorporating DFBETAS and Leverage significantly improves the accuracy of imputations, optimizes the balance between its sensitivity and specificity reduces bias, and enhances confidence interval coverage of imputed estimates, especially as the strength of the catch-all mechanism increases. The study demonstrates that MI with DFBETAS and Leverage outperforms standard MI methods, offering a robust solution for handling categorical data with catch-all MNAR mechanisms. This advancement in imputation methodology provides a more accurate and efficient means of dealing with missing data in various research fields.
First, Catch : Study of a Spring Meal
NOMINATED FOR THE 2018 ANDRE SIMON FOOD & DRINK BOOK OF THE YEARBBC RADIO 4 FOOD PROGRAMME BEST FOOD BOOKS OF 2018THE TIMES BEST FOOD BOOKS OF 2018FINANCIAL TIMES SUMMER FOOD BOOKS OF 2018'A one-off, the kind of food book that I believed was no longer being published... When I reached the last page, I went back to the beginning.'– Bee Wilson, The Times'A book as rich and rewarding as the rabbit stew he spends so many chapters making.'– Jenny Linford, Times Literary Supplement'A wonderful taste of fresh air... First, Catch is almost revolutionary... His words are delicious, musical heaven.'– William Sitwell‘Thom Eagle's writing is pure joy – effortless and unaffected. Even such a seemingly banal and simple thing as boiling vegetables is engaging and illuminating in his hands. He is easily one of my favourite writers, and this book deserves to become a classic.” – Olia Hercules, author of Mamushka and Kaukasis'It feels so tantalisingly transgressive to find a book that looks beautiful, feels lovely in the hand and just contains words — gorgeous, thoughtful essays... from a talented chef and writer.'- Tim Hayward, Financial Times‘The thing to do is just begin. The question, of course, is where?'So opens Thom Eagle's hymn to a singular early spring meal. A cookbook without recipes, this is an invitation to journey through the mind of a chef as they work. Stand next to Thom in the kitchen as he muses on the very best way to coax flavour out of an onion (slowly, and with more care than you might expect), or considers the crucial role of salt in the creation of the perfect assembly for early green shoots and leaves.In an era when we are so distracted that we eat almost without realising what we've just put in our mouth, this is food and writing to savour, gently steering the cook back towards simplicity, confidence and, above all, taste.
Association of weekend catch-up sleep with depression: A systematic review and meta-analysis
Background Weekend catch-up sleep (WCS) may alleviate weekday sleep deprivation, but its relationship with depression risk remains unclear. This systematic review and meta-analysis aims to explore the association between WCS and depression risk.Methods A comprehensive search was conducted in PubMed, Cochrane Library, Embase, Web of Science, and Scopus for observational studies published up to June 1, 2024. Data extraction and bias assessment were independently performed by two reviewers. Odds ratios (ORs) and 95 % confidence intervals (CIs) were calculated, with model selection based on the I2 statistic. Sensitivity analyses and publication bias tests were also conducted.Results A total of ten cross-sectional studies (326,871 participants) were included. Meta-analysis showed that WCS was significantly associated with a reduced risk of depression (OR = 0.80, 95 % CI: 0.68–0.90). Subgroup analyses showed moderate amounts of WCS (0–2 h) may be protective, but WCS beyond 2 h had limited protective effects against depression. Qualitative analyses showed that the protective effect of WCS against depression was more pronounced in men and middle-aged adults, and was particularly applicable to those who were sleep-deprived on weekdays.Limitations The cross-sectional design of included studies limits causality inference, and the sample primarily represents populations from the United States and South Korea, potentially affecting generalizability.Conclusions Moderate WCS is associated with lower depression risk in those with weekday sleep deficits, while excessive WCS may have diminishing or adverse effects. Further research should examine optimal WCS duration and underlying mechanisms.
Prediction of fish (Coilia nasus) catch using spatiotemporal environmental variables and random forest model in a highly turbid macrotidal estuary
Fish populations in estuaries are declining due to the changes in environmental conditions and fishing pressures. The estuarine fish behaviour is highly variable, influenced by both upstream fluvial and downstream tidal conditions. This study aims to predict the catch per unit effort (CPUE) of the Japanese Grenadier Anchovy (Coilia nasus) in the Chikugo River estuary by analyzing an extensive dataset of hourly fish catches and environmental variables through Random Forest (RF) models. The fish catch data for C. nasus, collected at 14.6–16 km upstream from the river mouth during the spawning season of every year from 2009 to 2020 using traditional fishing methods, was used. Along with these catch records, hydro-environmental variables such as salinity, turbidity, and temperature were monitored during the same period. The longitudinal variation of these environmental variables along the estuary (0–16 km) was measured during a fortnightly tidal cycle in September 2010. A total of 32 models (M1-M32) were developed to identify the optimal set of environmental variables influencing CPUE. The analysis highlights the significant impact of variables such as salinity, suspended sediment concentration (SSC), temperature, river discharge, and mean tidal range on CPUE. The results revealed that model M19, which incorporated salinity, SSC, and discharge, achieved the highest predictive accuracy (R2 = 0.89) and closely matched actual field conditions. Further, the results agree with previous research, as spatial distribution plots showed a preference for mature C. nasus habitats 15–16 km upstream from the river mouth. Additionally, the study found that temperature had a negligible effect on short-term CPUE predictions, likely due to its pronounced seasonal variability, suggesting that temperature may not be a critical factor for short-term CPUE predictions. This study highlights the significance of utilizing environmental variables to predict CPUE, emphasizing their role in understanding fish catch dynamics across spatiotemporal variations. The findings provide valuable insights for fisheries management, particularly in optimizing fishing zones based on environmental conditions to improve catch efficiency.