Set of graphs, tables or maps with demographic data on the population of the city of Zaragoza, gathered by the Statistical Observatory. http://www.zaragoza.es/ciudad/risp/demografia.htmÍndice content (complete): Municipal Register: Characteristics of the population Demographic Indicators Official Data Characteristics of the population Natural Population Movement Migrations Demographic Indicators Aid: To access the data set, you need to click on the Demographic and Population tag in the drop-down menu. There you can access the set of tables that are organised thematically. Click on the tag of the theme on which you want to extract the statistical data, select the desired table(s), clicking on the square to the left of the title of each table and clicking on the tab called: Table/Map/Graph to display or generate charts, tables and maps. http://demografia.zaragoza.es/ayuda.aspx?id_idioma=1Más information about the tool Developer Tools: You can export graphs in different formats and generate Json from the tab tables/maps in those indicators that have information together

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

This dataset is historical only and ends at 5/7/2021. For more information, please see http://dev.cityofchicago.org/open%20data/data%20portal/2021/05/04/covid-19-testing-by-person.html. The recommended alternative dataset for similar data beyond that date is https://data.cityofchicago.org/Health-Human-Services/COVID-19-Daily-Testing-By-Test/gkdw-2tgv.

This is the source data for some of the metrics available at https://www.chicago.gov/city/en/sites/covid-19/home/latest-data.html.

For all datasets related to COVID-19, see https://data.cityofchicago.org/browse?limitTo=datasets&sortBy=alpha&tags=covid-19.

This dataset contains counts of people tested for COVID-19 and their results. This dataset differs from https://data.cityofchicago.org/d/gkdw-2tgv in that each person is in this dataset only once, even if tested multiple times. In the other dataset, each test is counted, even if multiple tests are performed on the same person, although a person should not appear in that dataset more than once on the same day unless he/she had both a positive and not-positive test.

Only Chicago residents are included based on the home address as provided by the medical provider.

Molecular (PCR) and antigen tests are included, and only one test is counted for each individual. Tests are counted on the day the specimen was collected. A small number of tests collected prior to 3/1/2020 are not included in the table.

Not-positive lab results include negative results, invalid results, and tests not performed due to improper collection. Chicago Department of Public Health (CDPH) does not receive all not-positive results.

Demographic data are more complete for those who test positive; care should be taken when calculating percentage positivity among demographic groups.

All data are provisional and subject to change. Information is updated as additional details are received.

Data Source: Illinois National Electronic Disease Surveillance System

HDP_survey.csv and HDP_plots.csv *** ## Associated Data Paper The complete metadata for these data sets, including detailed descriptions of why and how the data were collected and validated, are in the following Data Paper: Bruna,E.M., M.Uriarte, M.Rosa Darrigo, P.Rubim, C.F.Jurinitz, E.R.Scott, O.Ferreira da Silva, & W.John Kress. 2023. Demography of the understory herb Heliconia acuminata (Heliconiaceae) in an experimentally fragmented tropical landscape. Ecology. ## Overview This file comprises 11 years (1998-2009) of demographic data from populations of the Amazonian understory herb Heliconia acuminata (LC Rich.) found at Brazil's Biological Dynamics of Forest Fragments Project (BDFFP). The dataset comprises >66,000 plant x year records of 8586 plants, including 3464 seedlings established after the first census. Seven populations were in experimentally isolated fragments (one in each of four 1-ha fragments and one in each of three 10-ha fragments), with the remaining six populations in continuous forest. Each population was in a 50xx 100 m permanent plot, with the distance between plots ranging from 500 m-60 km. The plants in each plot were censused annually, at which time we recorded, identified, marked, and measured new seedlings, identified any previously marked plants that died, and recorded the size of surviving individuals. Each plot was also surveyed 4-5 times during the flowering season to identify reproductive plants and record the number of inflorescences each produced. This data set describes the demographic plots in which surveys were conducted (HDP_plots.csv) and the demographic survey data (HDP_survey.csv). ## Description of the data and file structure: HDP_survey.csv * Format and storage mode: ASCII text, comma delimited. No compression scheme used. * Header information: The first row of the file contains the variable names. * Variables: -- plot_id: Plot in which plant is located (values: FF1-FF7\, CF1-CF6) -- subplot: Subplot in which plant is located (values: A1-E10 except in CF3\, where F6-J101) -- plant_id: Unique ID no. assigned to plant (values: range = 1-8660\, units: number\, precision: 1) -- tag_number: Number on tag attached to plant (values: range = 1-3751\, units: number\, precision: 1) -- year: Calendar year of survey (values: range = 1998-2009\, units: year\, precision: 1)) -- shts: No. of shoots when surveyed (values: range = 0-24\, units: shoots\, precision: 1\, NA: data missing) -- ht: Plant height when surveyed (values: range = 0-226\, units: cm\, precision: 1\, NA: data missing) -- infl: No. of inflorescences (if flowering) (values: range = 1-7\, units: shoots\, precision: 1\, NA: data missing) -- recorded_sdlg: New seedling (values: TRUE\, FALSE) -- adult_no_tag: Established (i.e.\, post-seedling) individual without tag (values: TRUE\, FALSE) -- treefall_status: Plant found under fallen tree crown\, branches\, or leaf litter at time of survey (values: branch = under fallen tree limbs tree = under tree crown or fallen trees litter = under accumulated leaf-litter NA = not relevant or no observation recorded) -- census_status: Plant status in a census (values: measured = alive\, measured dead = died prior to census missing = not found during census) ## Description of the data and file structure: HDP_plots.csv * Format and storage mode: ASCII text, comma delimited. No compression scheme used. * Header information: The first row of the file contains the variable names. * Variables: -- plot_id: Code used to identify a plot (Values: FF1-FF7 = plots in fragments\, CF1-CF6 = plots in continuous forest) -- habitat: Habitat in which a plot is located (Values: one = 1-ha fragment\, ten = 10-ha fragment\, forest = continuous forest) -- ranch: Ranch in which a plot is located (Values: porto alegre\, esteio\, dimona) -- bdffp_no: BDFFPs Reserve ID Number (Values: 1104\, 1202\, 1301\, 1501\, 2107\, 2108\, 2206\, 3209\, 3402\, NA) -- yr_isolated: for fragments\, the year they were initially isolated by felling (and in some cases burning) the trees surrounding them ## Describe relationships between data files, missing data codes, other abbreviations used. Be as descriptive as possible. * Missing values are represented with NA. ## Sharing/Access information * Though we welcome opportunities to collaborate with interested users, there are no restrictions on the use this data set. However, we do request that those using the data for teaching or research inform us of how they are doing so and cite the Bruna et al. Data Paper in Ecology and this Dryad archive. * Any publication using the data must include a BDFFP Technical Series Number in the Acknowledgments. Authors can request this series number upon the acceptance of their article by contacting the BDFFP's Scientific Coordinator or E. M. Bruna....

Build a RBM using this dataset to predict whether a particular user will like a movie or not. This data set contains 10000054 ratings and 95580 tags applied to 10681 movies by 71567 users of the online movie recommender service. Users were selected at random for inclusion. All users selected had rated at least 20 movies. Unlike previous MovieLens data sets, no demographic information is included. Each user is represented by an id, and no other information is provided. The data are contained in three files, movies.dat, ratings.dat and tags.dat. Also included are scripts for generating subsets of the data to support five-fold cross-validation of rating predictions.

User Ids Movielens users were selected at random for inclusion. Their ids have been anonymized.

Users were selected separately for inclusion in the ratings and tags data sets, which implies that user ids may appear in one set but not the other.

The anonymized values are consistent between the ratings and tags data files. That is, user id n, if it appears in both files, refers to the same real MovieLens user.

Ratings Data File Structure All ratings are contained in the file ratings.dat. Each line of this file represents one rating of one movie by one user, and has the following format:

UserID::MovieID::Rating::Timestamp

The lines within this file are ordered first by UserID, then, within user, by MovieID.

Ratings are made on a 5-star scale, with half-star increments.

Timestamps represent seconds since midnight Coordinated Universal Time (UTC) of January 1, 1970.

Tags Data File Structure All tags are contained in the file tags.dat. Each line of this file represents one tag applied to one movie by one user, and has the following format:

UserID::MovieID::Tag::Timestamp

The lines within this file are ordered first by UserID, then, within user, by MovieID.

Tags are user generated metadata about movies. Each tag is typically a single word, or short phrase. The meaning, value and purpose of a particular tag is determined by each user.

Timestamps represent seconds since midnight Coordinated Universal Time (UTC) of January 1, 1970.

Movies Data File Structure Movie information is contained in the file movies.dat. Each line of this file represents one movie, and has the following format:

MovieID::Title::Genres

MovieID is the real MovieLens id.

Movie titles, by policy, should be entered identically to those found in IMDB, including year of release. However, they are entered manually, so errors and inconsistencies may exist.

Genres are a pipe-separated list, and are selected from the following:

Action Adventure Animation Children's Comedy Crime Documentary Drama Fantasy Film-Noir Horror Musical Mystery Romance Sci-Fi Thriller War Western

This data set contains records for all tags applied to Hawaiian monk seals since 1981. These tags were applied by PSD personnel and cooperating scientists as part of the ongoing monk seal population assessment efforts. In addition, this data set contains tagging records for all seals that were tagged by USFWS in earlier years, and still present in the population when the current research effort began. These tagging records extend as far back as 1967. The remainder of the USFWS tagging data (data for seals that disappeared prior to the inception of the Hawaiian Monk Seal Research Program) are housed at PIFSC in paper form.

These data were compiled to help understand how climate change may impact dryland pinyon-juniper ecosystems in coming decades, and how resource management might be able to minimize those impacts. Objective(s) of our study were to model the demographic rates of PJ woodlands to estimate the areas that may decline in the future vs. those that will be stable. We quantified populations growth rates across broad geographic areas, and identified the relative roles of recruitment and mortality in driving potential future changes in population viability in 5 tree species that are major components of these dry forests. We used this demographic model to project pinyon-juniper population stability under future climate conditions, assess how robust these projected changes are, and to identify where on the landscape management strategies that decrease tree competition would effectively resist population decline. These data represent estimated recruitment, mortality and population growth across the distribution of five common pinyon-juniper species across the US Southwest. These data were collected by the US Forest service in their monitoring program, which is a systematic survey of forested regions across the entire US. Our data is from western US states, including AZ, CA, CO, ID, MT, NM, ND, NV, OR, SD, TX, UT, and was collected between 2000-2007, depending on state census collection times. These data were collected by the Forest Inventory and Analysis program of the USDA US Forest Service. Within each established plot, all adult trees greater than 12.7 cm (5 in.) diameter at breast height (DBH) are assigned unique tags and tracked within four, 7.32 m (24 ft.) radius subplots. All saplings <12.7 cm & > 2.54 cm (1 in.) DBH are assigned unique tags and tracked within four, 2.07 m (6.8 ft.) radius microplots within the larger adult plots. Finally, seedlings <2.54 cm DBH are counted within the same microplots as the saplings. Two censuses were conducted 10 years apart in each plot. These data can be used to inform how tree species have unique responses to changing climate conditions and how management actions, like tree density reduction, may effectively resist transformation away from pinyon-juniper woodland to other ecosystem types.

The Census Planning Database is produced by the U.S. Census Bureau. It assembles a range of housing, demographic, socioeconomic, and census operational data that can be used for survey and census planning.

The Planning Database uses selected Census and selected 2012-2016 American Community Survey (ACS) estimates. In addition to variables extracted from the census and ACS databases, operational variables include the 2010 Census Mail Return Rate for each block group and tract.

This dataset is a subset of the 2018 Census Planning Database, filtered for the state of Connecticut, and including variables relating to population. Variables relating to geography, households, housing units, census operations, and hard to count populations at the tract and block level can also be found on the CT Data Portal with the tag "Census 2020."

The Census Planning Database is produced by the U.S. Census Bureau. It assembles a range of housing, demographic, socioeconomic, and census operational data that can be used for survey and census planning.

The Planning Database uses selected Census and selected 2012-2016 American Community Survey (ACS) estimates. In addition to variables extracted from the census and ACS databases, operational variables include the 2010 Census Mail Return Rate for each block group and tract.

This dataset is a subset of the 2018 Census Planning Database, filtered for the state of Connecticut, and including variables relating to population. Variables relating to geography, households, housing units, census operations, and hard to count populations at the tract and block level can also be found on the CT Data Portal with the tag "Census 2020."

The BCPITTAGS database is used to store data from an Oncorhynchus mykiss (steelhead/rainbow trout) population dynamics study in Big Creek, a coastal stream along the Big Sur coast in Monterey County, California. The Landscape Ecology team at the Fisheries Ecology Division in Santa Cruz, CA is investigating the life history of this relatively small O. mykiss population to determine its significance in the persistence of the larger South-Central California Coast Steelhead Distinct Population Segment (DPS), which includes anadromous O. mykiss populations from the Pajaro River up to (but not including) the Santa Maria River, to see how these small coastal streams with little human-related impacts may contribute to DPS viability and resiliency. The database stores data from mark-recapture surveys, fish movement data collected via instream PIT tag readers, and stream environmental data. The data will be assimilated into a stage-structured population model, where stages include life history stage and location. Movement and survival rates will be determined and analyzed using data from the stationary PIT tag readers and mobile tracking devices.

# Dataset description

1. Single stellar model grids at Zsun

All single stellar model grids are labeled as "single_star_grid_Zsun_*.h5".
To access the dataset you should use the POSYDON v1 code and refer to the
code documentation, see https://github.com/POSYDON-code/POSYDON/releases/tag/v1.0.0.
The data can be accessed, e.g., with POSYDON as

```py
from posydon.grids.psygrid import PSyGrid
grid = PSyGrid()
grid.load(path_to_grid)
print(grid.initial_values)
print(grid.final_values)
print(grid[0].history1['log_L'])
print(grid[0].history1['log_Teff'])
```

2. Binary black hole population at Zsun

We provide the binary black hole population in the file labeled
"BBH_population_Zsun.h5". This file contains all binary systems forming
binary black holes as simulated from a population synthesis model of 50 million
binaries generated with POSYDON v1 at solar metallicity. The data can be
accessed, e.g., with Pandas as

```py
import pandas as pd
df = pd.read_hdf('./data_release/source_data/BBH_population_Zsun.h5',
key='history')
df.head(10)
```

For convenience we also provide a dataset labeled "arXiv_2212.10924.csv.gz"
that contains the subset of merging binary black holes obtained from
"BBH_population_Zsun.h5" at the time of double compact object formation used to
generate the Figure 4 of the main manuscript and compute the binary black hole
rates using the software released on https://github.com/ssbvr/BBH_merging_rates.
The data can be accessed, e.g., with Pandas as

```py
import pandas as pd
df = pd.read_csv('./data_release/source_data/arXiv_2212.10924.csv.gz',
compression='gzip')
df.head(10)
```

Please refer to the following notebook on how use the data to compute the
detectable and intrinsic merging population as well as rates, see
https://github.com/ssbvr/BBH_merging_rates/blob/main/arXiv_221210924_POSYDON_BBH_Zsun.ipynb

Return Data for Red Crab Tagged

Project Leader:
Richard A. Wahle, Bigelow Laboratory for Ocean Sciences
Additional Participants:
Jon Williams, Benthic Fishing Corp.
Yong Chen, University of Maine

Companion objects:
red crab sampling data
red crab tag data
redcrab camera data
redcrab temp data
red crab trawl log data
redcrab trawl data.

"The objectives of the main project were to: (1) Employ camera-based and net-trawl sampling methodology established by an earlier NMFS red crab surveys (Wigley et al. 1975) to determine wheter abundance, size structure, and sex composition of the population has changed significantly at the same sites sampled in 1974, (2) Conduct sea sampling to better characterize the commercail catch, (3) Conduct tagging to obtain much needed information on red crab growth rates and movement, and (4) Develop three stock assessment modeling approaches of different complexities (size-structured yield-per-recruit model, production model, and size-structuredied simulation model) to evaluate the dynamics of the red crab stock, estimate current status of the fishery, and evaluate alternative management strategies. The supplemental project compared the efficacy of otter-trawl to net trawl in this application.

The benthic sled system for camera surveys combined with net trawl collection generated the first population density estimates and demographic data of red crab in 30 years. The comparison of the two net trawl methods confirmed that otter trawls were the most efficient approach in these surveys. Results of the main project indicated that the abundance of the largest crabs targeted early in the history of the fishery (males>114 mm, 4.5 inches) is down by approximately 42% since 1974. Based on sea sampling data the fishery now harvests smaller male crabs, and the standing biomass of crabs currently harvested is on a par with 1974 levels. The abundance of smaller males and females is substantially higher than in 1974. Some 9600 crabs were tagged over the course of the study, and of about 300 returns there was little evidence of growth, which is consistent with prior evidence of slow growth for this species. However, the limited growth data curtailed application of the stock assessment models. The full parameterization of these models awaits addtional growth data. Models are implemented as Excel spread sheets that and are available from the PI, and will be easy for the user to update as data become available. These results were a key component of the NMFS red crab stock assessment conducted in 2006. "(extracted from: Final Report Submitted to the NORTHEAST CONSORTIUM, December 11, 2006)

Questions regarding this data set should be directed to:
Richard A. Wahle
Bigelow Laboratory for Ocean Sciences
P.O. Box 475
West Boothbay Harbor, ME
04575

Phone: 207 633-9659
E-mail: rwahle@bigelow.org

users.csv User_id: Unique identifier of user Country_code: Country code where the user registered assets.csv Show_type: Type of content, whether the asset is a movie or an episode of a TV series Genre: Genre of content Running_miutes: Runtime of content (Playable number of minutes) Source_language: Production language of content Asset_id: Unique identifier of video content at the most granular level (a movie or an episode of a TV series) Season_id: Unique identifier of content at season level. This is only applicable to TV series Series_id: Unique identifier of content at series level. This is only applicable to TV series Studio_id: Unique identifier of production studio for the content plays.csv Platform: Platform of consumption Minutes_viewed : Total number of minutes viewed, rounded to the nearest integer (0 means less than 30 seconds) Demographics.csv Psychographics.csv The dataset identifies psychographic and demographic tags about some iflix users. Each user-tag pair has an associated confidence score (1 is the highest, and 0 is the lowest confidence). Each trait can have up to 3 levels, depending on its granularity. Some traits can be identified by only considering the first two levels. At the same time, there are others that make more sense when all the three levels are considered, e.g., ‘iflix Viewing Behaviour’ is a level 2 psychographic trait that only makes sense when it is looked at in combination with the level 3 traits corresponding to it (‘casual,’ ‘player’ and ‘addict’). These traits represent different levels of viewing behavior of iflix users. Casual users have less than five viewing days in a month, player users have 5 to 12 viewing days in a month, and people with an addiction have more than 12 viewing days in a month. Traits are available corresponding to a user_id in the dataset only if we have certain confidence that the user belongs to the trait. Column and Description Level_1: Identifies the first level of the trait (psychologic or demographic) Level_2: Identifies the second level of the trait (e.g., Music Lovers, Movies Lovers) Level_3 : Identifies the third level of the trait, if available/relevant (e.g. Malay Movies Lovers, Indonesian TV Fans) Confidence_score: Confidence in associating the said trait (level_1, level_2, level_3) with the user

The dataset includes data on all fur seals tagged at Macquarie Island since 1989. The dataset includes information on the sex and species of individuals, information on their reproductive histories, resight data and tagging history.

The program began in 1986, but no data are available pre-1989.

The download file consists of a wide-range of files: an access database, a large number of excel spreadsheets, word documents, pdf files and text files. Data are contained in the access database (1994-1997) and excel spreadsheets and text files (all other years). The word documents and pdf files contain a lot of further information about the data collected in each season.

A readme document containing some general information about the datset is also part of the download file - in the top level directory.

The fields in this dataset are: date type ID number tag tagged previous tag weight digit sole width headgaurd muzzle coat belly biopsy blood milk girth length sex birth date weaning date birth mass mass at weaning date of weaning death date comments mother tag breeding father last seen year status territory

2007/2008 Season update A successful field season was undertaken at Macquarie Island during the 07/08 summer. This included maintenance of the annual surveys of pup production (DNA sampling for species identification), pup tagging and resighting of individual seals for assessment of reproductive performance and survival for long-term demographic analyses. Two publications in the journal Molecular Ecology on reproductive success of hybrids and mating strategies to limit hybridisation were produced, and the preparation of a major manuscript on the colonisation, status and trends in abundance of the three fur seal species at Macquarie Island has been completed and will be submitted shortly.

Progress has been made of three main fronts: 1. Completed field season at Macquarie Island and maintenance of the annual surveys of pup production (DNA sampling for species identification), pup tagging and resighting of individual seals for assessment of reproductive performance and survival for long-term demographic analyses. 2. Two publications in the journal Molecular Ecology on reproductive success of hybrids and mating strategies to limit hybridisation, 3. The preparation of a major manuscript on the colonisation, status and trends in abundance of the three fur seal species at Macquarie Island. We plan to make significant developments in demographic database management and analyses over the 08/09.

Taken from the 2008-2009 Progress Report: Project objectives: Background 1986-2008 The 'conservation and management of fur seals in the Antarctic marine ecosystem' research program (hereafter referred to as "the fur seal program") aims to provide key performance measures for recovering fur seal populations, and key Antarctic State of the Environment indicators, to monitor how biological and physical oceanographic change in Southern Ocean ecosystems, effects the reproductive performance of high trophic-level predators such as fur seals. Fur seals were the most heavily exploited of all of the Antarctic marine biota, and populations on both of Australia's subantarctic islands (Macquarie and the Heard and MacDonald Islands, HIMI), have yet to recover to pre-sealing numbers.

Over the last twenty years (1986-2007), research undertaken on this and former programs (managed by Dr Peter Shaughnessy) have aimed to provide information on: - the population status and ecology of recovering fur seal populations on Macquarie and Heard Islands, - species identification and composition, - the extent, trends, processes and implications of hybridisation among fur seals at Macquarie Island, - the impact of commercial sealing on the genetic variation and population structure of southern ocean fur seal populations, - the foraging ecology and lactation strategies of fur seals at Heard and Macquarie Islands, - the trophic linkages between fur seals and commercial fisheries at Macquarie and Heard Islands, and - how physical and biological oceanographic changes affect the reproductive performance of fur seals.

The fur seal program has successfully achieved these aims, and in doing so made significant contributions to implementing the many milestones of Australia's Antarctic Science Strategy (both past and present). In addition, the program has provided important advice on the conservation and management of Southern Ocean fur seal populations and marine systems, including: - providing information to Australian Fisheries Management Authority (AFMA) to assist ecological sustainable development (ESD) of the Patagonian toothfish fisheries around Macquarie and Heard Islands. - proving information to Environment Australia (now DEWR) on the distribution of fur seal foraging effort to assist planning and development of the Macquarie Island Marine Park. - providing specific data on the status of the subantarctic fur seal at Macquarie Island to DEWR, and assisting as a member of the subantarctic fur seal Recovery Team. - providing regular updates on the status of fur seal populations at Macquarie and Heard Islands to the SCAR Expert Group on Seals. - reporting to the Antarctic State of the Environment (Indicator 32).

The fur seal program is now one of the longest standing ongoing biological studies supported by the Australian Antarctic Division, providing an important time-series of population recovery following human exploitation, and most recently (after identification of sensitive demographic responses to small changes in sea surface temperatures), important ecological performance indicators and reference points that provide some of the best examples of how climate change may impact high trophic-level predator populations in the Southern Ocean.

The next five years (2008-2012) Over the next five years, the fur seal program aims to build on the above successes and continue core aspects population monitoring and demography. There will be a continued focus on undertaking research with clear applied management applications and a strong strategic focus targeting specific priorities of Australia's Antarctic Science Program Science Strategy. Applied applications include ESD of fisheries, MPA management and planning, acting on research and management priorities set out in the Department of the Environment and Heritage "The Action Plan for Australian Seals", the Recovery Plans for the Subantarctic fur seal and Antarctic State of the Environment reporting (SEO Indicator No. 32). All of these are in accord with and will help implement Australia's Oceans Policy.

The last five years of the fur seal program have seen considerable advancement in our understanding of the extent, trends and processes that facilitate and limit hybridisation among the three fur seal species at Macquarie Island. We have also identified highly significant relationships between fur seal reproductive success (fecundity and pup growth rates) and small changes to local sea surface temperature (STT) north of Macquarie Island associated with the subantarctic front. We also have a considerable data base on the survival and reproductive success of known-aged animals extending back to the early 1990s, and because of significant progress in developing molecular methods for identification of species and hybrids over the last five years, we now also have detailed genotype data for a large proportion of these seals (approx. 1,300).

With these data sets and knowledge, the focus of the fur seal program over the next five years will be to integrate molecular, demographic and oceanographic data to provide further insights into the how climatic and oceanographic changes in the Southern Ocean affect fur seal population on both annual and lifetime scales. The specific aims of the fur seal program will be to:

1. Maintain the population monitoring programs at Macquarie and Heard Islands
2. Maintain the long-term demographic program at Macquarie Island and analysis of data to determine age-specific survival and fecundity rates for each species and determine the reproductive costs of hybridisation.
3. Calculate annual changes in foraging ecology, survival, recruitment, reproductive rates and pup growth, and relate these to annual changes in regional oceanography.

The scientific relevance of these objectives are detailed below.

Progress against objectives: Progress has been made of three main fronts: 1. Field season at Macquarie Island during the 2008/09 summer has been completed. This included maintenance of the annual surveys of pup production (DNA sampling for species identification), pup tagging and resighting of individual seals for assessment of reproductive performance and survival for long-term demographic analyses. 2. A publication titled: "Fur seals at Macquarie Island: post-sealing colonisation, trends in abundance and hybridisation of three fur seals species" has been accepted for publication in Polar Biology. 3. Some database maintenance has been undertaken on the demographic database.

Taken from the 2010-2011 Progress Report: Public summary of the season progress: A successful field season was undertaken at Macquarie Island during the 10/11 season. This included maintenance of the annual surveys of pup production (DNA sampling for species identification), pup tagging and resighting of individual seals for assessment of reproductive performance and survival for long-term demographic analyses. A total of 255 pups were recorded this season, about an 8% increases since the 2009/10 season and more than any previous year since recolonisation. A new PhD program has commenced this year the focus will be analyses of the 25 year demographic dataset, and the impacts of climate change on population recovery.

This data set contains sea turtle length and weight measurements, sex ratios, species composition, capture and release locations, tagging information, and information on biological samples collected for loggerhead, green, and Kemp's Ridley sea turtle populations in the coastal waters of North Carolina. Sea turtles were double-tagged with Inconel Style 681 tags (National Band and Tag Company, Newport, Kentucky, USA) applied to the trailing edge of each rear flipper. Beginning in 1995, all turtles were additionally tagged with 125 kHz unencrypted Passive Integrated Transponder (PIT) tags (Destron-Fearing Corp., South St. Paul, Minnesota, USA), injected subcutaneously above the second-most proximal scale of the trailing margin of the left front flipper to ensure identification of the turtle in the event that both Inconel tags were lost. SCL and CCL (notch-to-tip and notch-to-notch) along with SCW and CCW were measured and recorded to the nearest 0.1 cm. Blood samples were collected from the dorsal cervical sinus of the turtle, and skin samples were collected from the trailing edge of the rear flippers. Scute scrapings were collected from the edge of the carapace

NOTE: This dataset replaces two previous ones. Please see below.

Chicago residents who are up to date with COVID-19 vaccines, based on the reported address, race-ethnicity, sex, and age group of the person vaccinated, as provided by the medical provider in the Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE).

“Up to date” refers to individuals who meet the CDC’s updated COVID-19 vaccination criteria based on their age and prior vaccination history. For surveillance purposes, up to date is defined based on the following criteria:

People ages 5 years and older: · Are up to date when they receive 1+ doses of a COVID-19 vaccine during the current season.

Children ages 6 months to 4 years: · Children who have received at least two prior COVID-19 vaccine doses are up to date when they receive one additional dose of COVID-19 vaccine during the current season, regardless of vaccine product. · Children who have received only one prior COVID-19 vaccine dose are up to date when they receive one additional dose of the current season's Moderna COVID-19 vaccine or two additional doses of the current season's Pfizer-BioNTech COVID-19 vaccine. · Children who have never received a COVID-19 vaccination are up to date when they receive either two doses of the current season's Moderna vaccine or three doses of the current season's Pfizer-BioNTech vaccine.

This dataset takes the place of two previous datasets, which cover doses administered from December 15, 2020 through September 13, 2023 and are marked has historical: - https://data.cityofchicago.org/Health-Human-Services/COVID-19-Daily-Vaccinations-Chicago-Residents/2vhs-cf6b - https://data.cityofchicago.org/Health-Human-Services/COVID-19-Vaccinations-by-Age-and-Race-Ethnicity/37ac-bbe3.

Data Notes:

Weekly cumulative totals of people up to date are shown for each combination of race-ethnicity, sex, and age group. Note that race-ethnicity, age, and sex all have an option for “All” so care should be taken when summing rows.

Coverage percentages are calculated based on the cumulative number of people in each race-ethnicity/age/sex population subgroup who are considered up to date as of the week ending date divided by the estimated number of people in that subgroup. Population counts are obtained from the 2020 U.S. Decennial Census. Actual counts may exceed population estimates and lead to coverage estimates that are greater than 100%, especially in smaller demographic groupings with smaller populations. Additionally, the medical provider may report incorrect demographic information for the person receiving the vaccination, which may lead to over- or underestimation of vaccination coverage. All coverage percentages are capped at 99%.

Weekly cumulative counts and coverage percentages are reported from the week ending Saturday, September 16, 2023 onward through the Saturday prior to the dataset being updated.

All data are provisional and subject to change. Information is updated as additional details are received and it is, in fact, very common for recent dates to be incomplete and to be updated as time goes on. At any given time, this dataset reflects data currently known to CDPH.

Numbers in this dataset may differ from other public sources due to when data are reported and how City of Chicago boundaries are defined.

The Chicago Department of Public Health uses the most complete data available to estimate COVID-19 vaccination coverage among Chicagoans, but there are several limitations that impact our estimates. Individuals may receive vaccinations that are not recorded in the Illinois immunization registry, I-CARE, such as those administered in another state, causing underestimation of the number individuals who are up to date. Inconsistencies in records of separate doses administered to the same person, such as slight variations in dates of birth, can result in duplicate records for a person and underestimate the number of people who are up to date.

For all datasets related to COVID-19, see https://data.cityofchicago.org/browse?limitTo=datasets&sortBy=alpha&tags=covid-19.

Data Source: Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE), U.S. Census Bureau 2020 Decennial Census

Inputs

The synthetic population was generated from the 2010-2014 ACS PUMS housing and person files.


United States Department of Commerce. Bureau of the Census. (2017-03-06).
American Community Survey 2010-2014 ACS 5-Year PUMS File [Data set].
Ann Arbor, MI: Inter-university Consortium of Political and Social
Research [distributor]. http://doi.org/10.3886/E100486V1

Persistent URL: http://doi.org/10.3886/E100486V1

Funding support

This work is supported under Grant G-2015-13903 from the Alfred P. Sloan Foundation on "The Economics of Socially-Efficient Privacy and Confidentiality Management for Statistical Agencies" rel="nofollow" target="_blank">https://www.ilr.cornell.edu/labor-dynamics-institute/research/project-19)" (PI: John M. Abowd)

Outputs

There are 17 housing files (data/housing)
- repHus0.csv, repHus1.csv, ... repHus16.csv
and 32 person files (data/person)
- rep_recode_ACSpus0.csv, rep_recode_ACSpus1.csv, ... rep_recode_ACSpus31.csv.

Files are split to be roughly equal in size. The files contain data for the entire country. Files are not split along any demographic characteristic. The person files and housing files must be concatenated to form a complete person file and a complete housing file, respectively.

Programs

Programs that generated this release can be found at https://github.com/labordynamicsinstitute/SynUSpopulation/releases/tag/v201703-beta and http://doi.org/10.5281/zenodo.556424

Additional information

For more information, see README.md

This dataset contains GLS ear-tag data collected from 2012 to 2021. This dataset was used in Merkel et al. 2023 Light-level geolocation as a tool to monitor polar bear (Ursus maritimus) denning ecology: a case study. Animal Biotelemetry. DOI 10.1186/s40317-023-00323-4.

Quality

The dataset contains raw light level logger outputs as wel las a metadata xlsx file.

With extensive coverage nationally and across various languages, our B2C Language Demographic Data provides valuable insights for sales, marketing, and research purposes. Whether you're seeking to expand your client base, enhance lead generation efforts, or conduct market analysis, our dataset empowers you to make informed decisions and drive business growth.

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Key Tags:

Data Enrichment, B2C Sales, Analytics, People Data, B2C, Customer Data, Prospect Data, Audience Generation, B2C Data Enrichment, Business Intelligence, AI / ML, Market Intelligence, Segmentation, Audience Targeting, Audience Intelligence, B2C Advertising, List Validation, Data Cleansing, Competitive Intelligence, Demographic Data, B2C Data, Lead Information, Data Append, Data Augmentation, Data Cleansing, Data Enhancement, Data Intelligence, Data Science, Due Diligence, Marketing Data Enrichment, Master Data Enrichment, People-Based Marketing, Predictive Analytics, Prospecting, Sales Intelligence, Sales Prospecting

Analysis of ‘COVID-19 Daily Testing - By Person - Historical’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/b4404dbf-ef90-4b04-be4e-a00cdb02ba18 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

This dataset is historical only and ends at 5/7/2021. For more information, please see http://dev.cityofchicago.org/open%20data/data%20portal/2021/05/04/covid-19-testing-by-person.html. The recommended alternative dataset for similar data beyond that date is https://data.cityofchicago.org/Health-Human-Services/COVID-19-Daily-Testing-By-Test/gkdw-2tgv.

This is the source data for some of the metrics available at https://www.chicago.gov/city/en/sites/covid-19/home/latest-data.html.

For all datasets related to COVID-19, see https://data.cityofchicago.org/browse?limitTo=datasets&sortBy=alpha&tags=covid-19.

This dataset contains counts of people tested for COVID-19 and their results. This dataset differs from https://data.cityofchicago.org/d/gkdw-2tgv in that each person is in this dataset only once, even if tested multiple times. In the other dataset, each test is counted, even if multiple tests are performed on the same person, although a person should not appear in that dataset more than once on the same day unless he/she had both a positive and not-positive test.

Only Chicago residents are included based on the home address as provided by the medical provider.

Molecular (PCR) and antigen tests are included, and only one test is counted for each individual. Tests are counted on the day the specimen was collected. A small number of tests collected prior to 3/1/2020 are not included in the table.

Not-positive lab results include negative results, invalid results, and tests not performed due to improper collection. Chicago Department of Public Health (CDPH) does not receive all not-positive results.

Demographic data are more complete for those who test positive; care should be taken when calculating percentage positivity among demographic groups.

All data are provisional and subject to change. Information is updated as additional details are received.

Data Source: Illinois National Electronic Disease Surveillance System

--- Original source retains full ownership of the source dataset ---