Note: for total cholesterol, HDL cholesterol, systolic blood pressure, diastolic blood pressure, the mean and standard deviation (SD) are presented for each category.

The Southern Ocean is a remote, hostile environment where conducting marine biology is challenging, so we know relatively little about this important region, which is critical as a habitat for breeding and foraging of many marine endotherms. Scientists from around the world have been tracking seals, penguins, petrels, whales and albatrosses for more than two decades to learn how they spend their time at sea. The Retrospective Analysis of Antarctic Tracking Data (RAATD), was initiated by the SCAR Expert Group on Marine Mammals (EG-BAMM) in 2010. This team has assembled tracking data shared by 38 biologists from 11 different countries to accumulate the largest animal tracking database in the world, containing information from 15 species, containing over 3,400 individual animals and almost 2.5 million at-sea locations. Analysing a dataset of this size brings its own challenges and the team is developing new and innovative statistical approaches to integrate these complex data. When complete RAATD will provide a greater understanding of fundamental ecosystem processes in the Southern Ocean, help predict the future of top predator distribution and help with spatial management planning.

This HydroShare resource provides Jupyter Notebooks with instructions and code for accessing and subsetting the NOAA National Water Model CONUS Retrospective Dataset. There are two Jupyter Notebooks 1. NWM_output_variable_retrieval_with_FeatureID.ipynb 2. NWM_output_variable_retrieval_with_shapefile.ipynb The first retrieves data for one point (feature ID). The second retrieves data for areas specified interactively or via an uploaded shapefile. These notebooks programmatically retrieve the data from Amazon Web Services (https://registry.opendata.aws/nwm-archive/), and in the case of Zone data retrieval average the data over the zones specified. The notebooks provided are coded to retrieve data from NWM retrospective analysis version 3.0 released in ZARR format in December 2023. The NOAA National Water Model Retrospective dataset contains input and output from multi-decade CONUS retrospective simulations (https://registry.opendata.aws/nwm-archive/ ). These simulations used meteorological input from retrospective data. The output frequency and fields available in this historical NWM dataset differ from those contained in the real-time operational NWM forecast model. Additionally, note that no streamflow or other data assimilation is performed within any of the NWM retrospective simulations

As part of its implementation of Executive Order 13563, "Improving Regulation and Regulatory Review," the Department of Education sought comments and information from interested parties on the Department's "Preliminary Plan for Retrospective Analysis of Existing Rules".

Life Table Data: Field-based, partial life table data for immature stages of Bemisia tabaci on cotton in Maricopa, Arizona, USA. Data were generated on approximately 200 individual insects per cohort with 2-5 cohorts per year for a total of 44 cohorts between 1997 and 2010. Data provide the marginal, stage-specific rates of mortality for eggs, and 1st, 2nd, 3rd, and 4th instar nymphs. Mortality is characterized as caused by inviability (eggs only), dislodgement, predation, parasitism and unknown. Detailed methods can be found in Naranjo and Ellsworth 2005 (Entomologia Experimentalis et Applicata 116(2): 93-108). The method takes advantage of the sessile nature of immature stages of this insect. Briefly, an observer follows individual eggs or settled first instar nymphs from natural populations on the underside of cotton leaves in the field with a hand lens and determines causes of death for each individual over time. Approximately 200 individual eggs and nymphs are observed for each cohort. Separately, densities of eggs and nymphs are monitored with standard methods (Naranjo and Flint 1994, Environmental Entomology 23: 254-266; Naranjo and Flint 1995, Environmental Entomology 24: 261-270) on a weekly basis.
Matrix Model Data: Life table data were used to provide parameters for population matrix models. Matrix models contain information about stage-specific rates for development, survival and reproduction. The model can be used to estimate overall population growth rate and can also be analyzed to determine which life stages contribute the most to changes in growth rates. Resources in this dataset:Resource Title: Matrix model data from Naranjo, S.E. (2017) Retrospective analysis of a classical biological control program. Journal of Applied Ecology. File Name: MatrixModelData.xlsxResource Description: Life table data were used to provide parameters for population matrix models. Matrix models contain information about stage-specific rates for development, survival and reproduction. The model can be used to estimate overall population growth rate and can also be analyzed to determine which life stages contribute the most to changes in growth rates. Resource Title: Data Dictionary: Life table data. File Name: DataDictionary_LifeTableData.csvResource Title: Life table data from Naranjo, S.E. (2017) Retrospective analysis of a classical biological control program. Journal of Applied Ecology. File Name: LifeTableData.xlsxResource Description: Field-based, partial life table data for immature stages of Bemisia tabaci on cotton in Maricopa, Arizona, USA. Data were generated on approximately 200 individual insects per cohort with 2-5 cohorts per years for a total of 44 cohorts between 1997 and 2010. Data provide the marginal, stage-specific rates of mortality for eggs, and 1st, 2nd, 3rd, and 4th instar nymphs. Mortality is characterized as caused by inviability (eggs only), dislodgement, predation, parasitism and unknown. Detailed methods can be found in Naranjo and Ellsworth 2005 (Entomologia, Experimentalis et Applicata 116: 93-108). The method takes advantage of the sessile nature of immature stages of this insect. Briefly, an observer follows individual eggs or settled first instar nymphs from natural populations on the underside of cotton leaves in the field with a hand lens and determines causes of death for each individual over time. Approximately 200 individual eggs and nymphs are observed for each cohort. Separately, densities of eggs and nymphs are monitored with standard methods (Naranjo and Flint 1994, Environmental Entomology 23: 254-266; Naranjo and Flint 1995, Environmental Entomology 24: 261-270) on a weekly basis. Resource Title: Life table data from Naranjo, S.E. (2017) Retrospective analysis of a classical biological control program. Journal of Applied Ecology. File Name: LifeTableData.csvResource Description: CSV version of the data. Field-based, partial life table data for immature stages of Bemisia tabaci on cotton in Maricopa, Arizona, USA. Data were generated on approximately 200 individual insects per cohort with 2-5 cohorts per years for a total of 44 cohorts between 1997 and 2010. Data provide the marginal, stage-specific rates of mortality for eggs, and 1st, 2nd, 3rd, and 4th instar nymphs. Mortality is characterized as caused by inviability (eggs only), dislodgement, predation, parasitism and unknown. Detailed methods can be found in Naranjo and Ellsworth 2005 (Entomologia, Experimentalis et Applicata 116: 93-108). The method takes advantage of the sessile nature of immature stages of this insect. Briefly, an observer follows individual eggs or settled first instar nymphs from natural populations on the underside of cotton leaves in the field with a hand lens and determines causes of death for each individual over time. Approximately 200 individual eggs and nymphs are observed for each cohort. Separately, densities of eggs and nymphs are monitored with standard methods (Naranjo and Flint 1994, Environmental Entomology 23: 254-266; Naranjo and Flint 1995, Environmental Entomology 24: 261-270) on a weekly basis.

When panel data are not available, retrospective data are used in the estimation of dynamic choice models. However, retrospective data are not reliable. Previous studies of voting choices, for example, have shown that respondents misreport their past choices in order to appear more consistent with their current choice. Such retrospective bias leads to inconsistent estimates, especially when there is state dependence in choices. Specifically, observed persistence in retrospective data may be due to (a) true state dependence, (b) unobserved heterogeneity, and (c) retrospective bias in reporting previous choices. Whereas Heckman in his 1981 study deals with (a) and (b), we introduce a method to estimate true state dependence while accounting for both unobserved heterogeneity and retrospective reporting bias. Our method is based on modeling the reporting behavior and integrating it into the estimation. The identification strategy is based on the correlation between the reported previous choices and current exogenous variables. Using data on Israeli voters, we find that the probability that a respondent whose vote intention in 1991 differed from his or her past voting choices would lie about their past choices is 0.23.

Identifying the basic habitat requirements of Antarctic predators is fundamental to understanding how they will respond to the human-induced challenges of commercial fisheries and climate change. This understanding can only be achieved if the underlying linkages to physical processes are related to animal movements. As part of the international Retrospective Analysis of Antarctic Tracking Data (RAATD) organized by the SCAR Expert Group of Birds and Marine Mammals, this research will collate and synthesize tracking data from crabeater seals, Lobodon carcinophagus, and Weddell seals, Leptonychotes weddelli. These data will be combined with all available data from the Southern Ocean that has been collected by researchers from Norway, United Kingdom, Germany, Australia and the USA. These data will be analyzed using a common analytical approach and synthesized into a synoptic view of these two species across the Southern Ocean. The diving and movement patterns will be examined for each species. As well, the total home range and core habitat utilization patterns for each species and region will be determined. This study will develop global habitat maps for each species based on physical and biological attributes of their 'hot-spots' and then overlay all the species specific maps to identify multi-species areas of ecological significance. Broader impacts include support and training for a postdoctoral scholar, the production of a publicly available database and the participation in an international data synthesis effort.

A door-to-door survey was conducted to enumerate all household members by age and sex in a low-income community in Peru. 856 adults 35 years and older were eligible to participate in screening for type 2 diabetes and hypertension. 709 (83%) participated in screening. 130 (18.3%) were diagnosed with hypertension and/or type 2 diabetes of which 109 (84%) participated at program onset and 22 were added later from earlier non-participants in screening or program onset to form the cohort of 131 patients with diabetes and/or hypertension. The primary care program had components of the Chronic Care Model, community health workers, and freely accessible visits and medications. The program operated between September 2011 and May 2014, and consisted of two care periods (separated by a six-month hiatus), first a 10-month home-care period, then a 17-month clinic-care period. The dataset is two files corresponding to two exposures: the 27-month program overall (post- versus pre-) (N=262 observations, 131 pairs with patients as self-controls) and care period (clinic versus home), N=211 (109 home and 102 clinic observations, >131 because 80 patients participated in both care periods). Exposures were evaluated for their effects on guidelines-based pharmacotherapy standards: hypoglycemic and antihypertensive medications, low-dose aspirin, and first-line angiotensin converting enzyme inhibitor (ACEi) treatment of diabetes with elevated blood pressure. Methods From 2011 to 2014, data was collected prospectively, during weekly (home visits) or monthly (clinic visits), on paper encounter forms that were entered into Microsoft Excel as part of the standard operation of the community-based program. In January 2020, the University of Arizona institutional review board approved the use of the de-identified data for a study of the program's effects on clinical outcomes. Time-series data (fasting glucose and blood pressure) was collapsed on the median of monthly average fasting glucose and blood pressure values during the program (27 months) and the respective care periods, home (10 months) and clinic (17 months). Antihypertensive and hypoglycemic agents were collapsed on the highest dose ever received, angiotensin-converting enzyme inhibitors (ACEi) and aspirin on whether any dose was ever received, by treatment-eligible groups, and within program and care period time intervals. Retention in care was obtained by counting visits and elapsed months (from first to last patient encounters) during the program and care periods. Treatment-eligible groups were low-dose aspirin candidates (10-year cardiovascular disease (CVD) risk >=10% by the Framingham alternate model that uses clinical factors only, no laboratory factors; blood pressure (BP) treatment candidates (BP >=130/80 mm Hg if diabetic or >=140/90 mm Hg if non-diabetic); hypoglycemic agent candidates (patients with diabetes); and diabetic ACEi candidates (diabetes with BP >=130/80 mm Hg). Data has been transformed into two files corresponding to two exposures: 1) program, post- versus pre- (referent), N=262 observations; and 2) care period, clinic versus home (referent), N=211 observations. There are two data files in text (comma-delimited) format. Pre-post....csv contains the 262 observations for the program exposure. Care period....csv contains the 211 care period observations. Each file has a data dictionary also in comma-delimited format. "Pre-post data dict....csv" describes the variables in the program exposure study. "Care period data dict....csv" describes the variables in the care period exposure study.

Datasets from the Estonian Myocardial Infarction Register (large national register)and 6 Estonian hospitals including information abot cardiovascular riskfactos and previous health history and lab results of sGFR and HbA1c blood analysis. Data was used for an epidemiological retrospective cohort study.

This HydroShare resource is designed for retrieving and subsetting the NWM retrospective analysis datasets in Zarr format, which are hosted on the AWS bucket accessible at https://noaa-nwm-retrospective-3-0-pds.s3.amazonaws.com/index.html. The dataset offers spatial coverage across the entire CONUS and spans a time range from February 01, 1979, to January 31, 2023.

An OS was developed to assign COVID-19 disease severity using the Observational Medical Outcomes Partnership common data model within the National COVID Cohort Collaborative (N3C) data enclave. We then evaluated usefulness of the developed OS using heterogenous EHR data from January 2020 to October 2021 submitted to N3C by 63 healthcare organizations across the United States. Principal Components Analysis (PCA) was employed to characterize changes in disease severity among patients during the 28-day period following COVID-19 diagnosis.

.xls dataset containing clinical and neuropsychological data of HD patients included in the study described at title by Fondazione Besta

The NOAA National Water Model Retrospective dataset contains input and output from multi-decade CONUS retrospective simulations. These simulations used meteorological input fields from meteorological retrospective datasets. The output frequency and fields available in this historical NWM dataset differ from those contained in the real-time operational NWM forecast model. Additionally, note that no streamflow or other data assimilation is performed within any of the NWM retrospective simulations

One application of this dataset is to provide historical context to current near real-time streamflow, soil moisture and snowpack conditions. The retrospective data can be used to infer flow frequencies and perform temporal analyses with hourly streamflow output and 3-hourly land surface output. This dataset can also be used in the development of end user applications which require a long baseline of data for system training or verification purposes.


Details for Each Version of the NWM Retrospective Output

CONUS Domain - CONUS retrospective output is provided by all four versions of the NWM

1. Version 3.0 - A 44-year (February 1979 through January 2023) retrospective simulation using version 3.0 of the National Water Model.
2. Version 2.1 - A 42-year (February 1979 through December 2020) retrospective simulation using version 2.1 of the National Water Model.
3. Version 2.0 - A 26-year (January 1993 through December 2018) retrospective simulation using version 2.0 of the National Water Model.
4. Version 1.2 - A 25-year (January 1993 through December 2017) retrospective simulation using version 1.2 of the National Water Model.
   
   

oCOUNS Domains - Only v3.0 of the NWM Retrospective data set provides coverage of the NWM Alaska, Hawaii and Puerto Rico / US Virgin Island domains.

CONUS Meteorological Forcing

1. Versions 3.0 and 2.1: NWM Retrospective simulations used forcing from the Office of Water Prediction Analysis of Record for Calibration (AORC) dataset. NWM v2.1 used AORC v1.0 for 1979-2006 and AORC v1.1 for 2007-2020, while NWM v3.0 used AORC v1.1 for the full v3.0 (1979-2023 period)

Important Warning - While the metadata tag in the NWM v3.0 forcing files label the files as “v2.1”, the files are in fact v3.0 forcing files. 2) Versions 2.0 and 1.2 - NWM Retrospective simulation uses forcing from the North American Land Data Assimilation System (NLDAS) dataset
oCONUS Meteorological Forcing

1. Version 3.0 - AORC Alaska forcing was used to drive the NWM Alaska simulation, while North American Regional Reanalysis (NARR) data along with precipitation from the Alaska Pacific River Forecast Center (APRFC) was used to drive the Hawaii retrospective simulation. Similarly, the Puerto Rico / US Virgin Island retrospective simulation was driven by NARR data along with precipitation from the Southeast River Forecast Center.
   
   

Formats - NWM Retrospective data is available in two formats, NetCDF and Zarr. The NetCDF files contain the full set of NWM output data, while the Zarr files contain a subset of NWM output fields that vary with model version.

1. NWM V3.0: All model output and forcing input fields are available in the NetCDF format. All model output fields along with the precipitation forcing field are available in the Zarr format.
2. NWM V2.1: All model output and forcing input fields are available in the NetCDF format. Many of the model output fields along with the precipitation forcing field are available in the Zarr format
3. NWM V2.0: All model output fields are available in NetCDF format. Model channel output including streamflow and related fields are available in Zarr format.
4. NWM V1.2: All model output fields are available in NetCDF format.
   
   A table listing the data available within each NetCDF and Zarr file is located in the 'documentation page'. This data includes meteorological NWM forcing inputs along with NWM hydrologic and land surface outputs, and varies by version number.

OR = odds ratio, CI = confidence interval.Adjusted for clustering by practice.*Referent is none or no disease.

Hypothesis: To find the incidence and risk factors of acute kidney injury in arthroplasty patients.

Data showed that age, obesity, smoking and COPD are risk factors of AKI. Surprisingly, hypertension and renal dysfunction were not among the risk factors.

Data collection was done by manually retrieving patient information through electronic patient record (EPR).

Data from a study to critically examine some of the issues of using data from ToxRefDB, a database largely composed of guideline studies for pesticidal active ingredients, using a case study focusing on chemically-induced anemia. This dataset is associated with the following publication: Judson, R.S., M. Martin, G. Patlewicz, and C.E. Wood. (Reg. Tox. Pharm.) Retrospective Mining of Toxicology Data to Discover Multispecies and Chemical Class Effects: Anemia as a Case Study. REGULATORY TOXICOLOGY AND PHARMACOLOGY. Elsevier Science Ltd, New York, NY, USA, 86: 74-92, (2017).

The dataset is on TB patients from different categories of health facilities in Accra, Ghana.

Respiratory distress (RD) is higher among newborns born by caesarean section (CS) at term. RCOG recommend steroid administration for CS prior to 39 weeks. Evidence for effectiveness of steroids for neonatal RD at term is inconclusive. The racial differences are yet to be studied.

A single center retrospective cohort study was conducted in Colombo, Sri Lanka form December 2016 to February 2019. All mothers delivered by CS between 37+0 and 38+6 weeks were included in the study. Mothers with severe maternal hypertension, severe fetal rhesus sensitization, evidence of intrauterine infection, multiple pregnancies and those who received steroids due to other indications at a prior gestation and were excluded. Cohort was subdivided according to administration of IM dexamethasone prior to CS. Primary outcomes measured were RD, admissions to neonatal intensive care unit (NICU) and special care baby unit (SCBU). Neonatal infections and maternal duration of hospital stay was recorded as sec...

Minimal anonymized data set for replication of study findings

The Retrospective Analysis of Antarctic Tracking Data (RAATD) is a Scientific Committee for Antarctic Research (SCAR) project led jointly by the Expert Groups on Birds and Marine Mammals and Antarctic Biodiversity Informatics, and endorsed by the Commission for the Conservation of Antarctic Marine Living Resources. The RAATD project team consolidated tracking data for multiple species of Antarctic meso- and top-predators to identify Areas of Ecological Significance. These datasets constitute the compiled tracking data from a large number of research groups that have worked in the Antarctic since the 1990s.

This metadata record pertains to the "filtered" version of the data files. These files contain position estimates that have been processed using a state-space model in order to estimate locations at regular time intervals. For technical details of the filtering process, consult the data paper. The filtering code can be found in the https://github.com/SCAR/RAATD repository.

This data set comprises one metadata csv file that describes all deployments, along with data files (3 files for each of 17 species). For each species there is: - an RDS file that contains the fitted filter model object and model predictions (this file is RDS format that can be read by the R statistical software package) - a PDF file that shows the quality control results for each individual model - a CSV file containing the interpolated position estimates

For details of the file contents and formats, consult the data paper.

The data are also available in a standardized version (see https://data.aad.gov.au/metadata/records/SCAR_EGBAMM_RAATD_2018_Standardised) that contain position estimates as provided by the original data collectors (generally, raw Argos or GPS locations, or estimated GLS locations) without state-space filtering.