2022 Field Experiment: Row-Code of a leaf sample; Line-Genetic population; Date-Scoring date; VS-Visual score given.

Longitudinal data set of a nationally representative sample of the population aged 65 and over in Japan, comparable to that collected in the US and other countries. The first two waves of data are now available to the international research community. The sample is refreshed with younger members at each wave so it remains representative of the population at each wave. The study was designed primarily to investigate health status of the Japanese elderly and changes in health status over time. An additional aim is to investigate the impact of long-term care insurance system on the use of services by the Japanese elderly and to investigate the relationship between co-residence and the use of long term care. While the focus of the survey is health and health service utilization, other topics relevant to the aging experience are included such as intergenerational exchange, living arrangements, caregiving, and labor force participation. The initial questionnaire was designed to be comparable to the (US) Longitudinal Study of Aging II (LSOAII), and to the Asset and Health Dynamics Among the Oldest Old (AHEAD, a pre-1924 birth cohort) sample of the Health and Retirement Study (HRS), which has now been merged with the HRS. The sample was selected using a multistage stratified sampling method to generate 340 primary sampling units (PSUs). The sample of individuals was selected for the most part by using the National Residents Registry System, considered to be universal and accurate because it is a legal requirement to report any move to local authorities within two weeks. From each of the 340 PSUs, 6-11 persons aged 65-74 were selected and 8-12 persons aged 75+ were sampled. The population 75+ was oversampled by a factor of 2. Weights have been developed for respondents to the first wave of the survey to reflect sampling probabilities. Weights for the second wave are under development. With these weights, the sample should be representative of the 65+ Japanese population. In fall 1999, 4,997 respondents aged 65+ were interviewed, 74.6 percent of the initial target. Twelve percent of responses were provided by proxies, because of physical or mental health problems. The second wave of data was collected in November 2001. The third wave was collected in November 2003. Questionnaire topics include family structure, and living arrangements; subjects'''' parents/spouse''''s parents/children; socioeconomic status; intergenerational exchange; health behaviors, chronic conditions, physical functioning; activities of daily living and instrumental activities of daily living; functioning in the community; mental health depression measures; vision and hearing; dental health; health care and other service utilization. A CD is available which include the codebook and data files for the first and second waves of the national sample. The third wave of data will be released at a later date. * Dates of Study: 1999-2003 * Study Features: Longitudinal, International * Sample Size: ** 4,997 Nov/Dec 1999 Wave 1 ** 3,992 Nov 2001 Wave 2 ** Nov 2003 Wave 3 Link: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/00156

Estimates of population density are fundamental to wildlife conservation and management. Distance sampling from line transects is a widely used sample count method and is most often analysed using Distance software. However, this method has limited capabilities with mobile populations (e.g., birds), which tend to encounter an observer more often than immobile ones. This paper presents a novel distance sampling method based on a different set of models and assumptions, named WildlifeDensity after its associated software. It is based on mechanistic modelling of visual detections of individuals or groups according to radial distance from the observer or perpendicular distance from the transect line. It also compensates for population–observer relative movement to avoid the detection overestimates associated with highly mobile populations. The models are introduced in detail and then tested in three ways: 1) WildlifeDensity is applied to several ‘benchmark’ populations of known density and ..., Data were collected by line transect distance sampling of wildlife populations. Data were processed by analysis in the computer programs WildlifeDensity and Distance., , # Wildlife density estimation by distance sampling: A novel technique with movement compensation

Dataset DOI: 10.5061/dryad.ns1rn8q14

Description of the data and file structure

Files and variables

This folder contains the data for the article: Wildlife Density Estimation by Distance Sampling: A Novel Technique with Movement Compensation

[Access this dataset on Dryad: https://doi.org/10.5061/dryad.ns1rn8q14]

Author details: David G. Morgan1, John R. Gibbens1, Ed. T. Conway(dec)., Graham Hepworth2, James Clough1

1School of Biosciences, 2School of Mathematics and Statistics, University of Melbourne, Parkville, Australia

Correspondence: David G. Morgan. Email: d.morgan@unimelb.edu.au

The data are organised with reference to the associated figures, tables and/or sections in the article, as described below. Files with the extensions .xls and .xlsx are for use in the Microso...,

Abstract

The 2011 Population and Housing Census is the third national Census to be conducted in Namibia after independence. The first was conducted 1991 followed by the 2001 Census. Namibia is therefore one of the countries in sub-Saharan Africa that has participated in the 2010 Round of Censuses and followed the international best practice of conducting decennial Censuses, each of which attempts to count and enumerate every person and household in a country every ten years. Surveys, by contrast, collect data from samples of people and/or households.

Censuses provide reliable and critical data on the socio-economic and demographic status of any country. In Namibia, Census data has provided crucial information for development planning and programme implementation. Specifically, the information has assisted in setting benchmarks, formulating policy and the evaluation and monitoring of national development programmes including NDP4, Vision 2030 and several sector programmes. The information has also been used to update the national sampling frame which is used to select samples for household-based surveys, including labour force surveys, demographic and health surveys, household income and expenditure surveys. In addition, Census information will be used to guide the demarcation of Namibia's administrative boundaries where necessary.

At the international level, Census information has been used extensively in monitoring progress towards Namibia's achievement of international targets, particularly the Millennium Development Goals (MDGs).

The latest and most comprehensive Census was conducted in August 2011. Preparations for the Census started in the 2007/2008 financial year under the auspices of the then Central Bureau of Statistics (CBS) which was later transformed into the Namibia Statistics Agency (NSA). The NSA was established under the Statistics Act No. 9 of 2011, with the legal mandate and authority to conduct population Censuses every 10 years. The Census was implemented in three broad phases; pre-enumeration, enumeration and post enumeration.

During the first pre-enumeration phase, activities accomplished including the preparation of a project document, establishing Census management and technical committees, and establishing the Census cartography unit which demarcated the Enumeration Areas (EAs). Other activities included the development of Census instruments and tools, such as the questionnaires, manuals and field control forms.

Field staff were recruited, trained and deployed during the initial stages of the enumeration phase. The actual enumeration exercise was undertaken over a period of about three weeks from 28 August to 15 September 2011, while 28 August 2011 was marked as the reference period or 'Census Day'.

Great efforts were made to check and ensure that the Census data was of high quality to enhance its credibility and increase its usage. Various quality controls were implemented to ensure relevance, timeliness, accuracy, coherence and proper data interpretation. Other activities undertaken to enhance quality included the demarcation of the country into small enumeration areas to ensure comprehensive coverage; the development of structured Census questionnaires after consultat.The post-enumeration phase started with the sending of completed questionnaires to Head Office and the preparation of summaries for the preliminary report, which was published in April 2012. Processing of the Census data began with manual editing and coding, which focused on the household identification section and un-coded parts of the questionnaire. This was followed by the capturing of data through scanning. Finally, the data were verified and errors corrected where necessary. This took longer than planned due to inadequate technical skills.

Geographic coverage

National coverage

Analysis unit

Households and persons

Universe

The sampling universe is defined as all households (private and institutions) from 2011 Census dataset.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Sample Design

The stratified random sample was applied on the constituency and urban/rural variables of households list from Namibia 2011 Population and Housing Census for the Public Use Microdata Sample (PUMS) file. The sampling universe is defined as all households (private and institutions) from 2011 Census dataset. Since urban and rural are very important factor in the Namibia situation, it was then decided to take the stratum at the constituency and urban/rural levels. Some constituencies have very lower households in the urban or rural, the office therefore decided for a threshold (low boundary) for sampling within stratum. Based on data analysis, the threshold for stratum of PUMS file is 250 households. Thus, constituency and urban/rural areas with less than 250 households in total were included in the PUMS file. Otherwise, a simple random sampling (SRS) at a 20% sample rate was applied for each stratum. The sampled households include 93,674 housing units and 418,362 people.

Sample Selection

The PUMS sample is selected from households. The PUMS sample of persons in households is selected by keeping all persons in PUMS households. Sample selection process is performed using Census and Survey Processing System (CSPro).

The sample selection program first identifies the 7 census strata with less than 250 households and the households (private and institutions) with more than 50 people. The households in these areas and with this large size are all included in the sample. For the other households, the program randomly generates a number n from 0 to 4. Out of every 5 households, the program selects the nth household to export to the PUMS data file, creating a 20 percent sample of households. Private households and institutions are equally sampled in the PUMS data file.

Note: The 7 census strata with less than 250 households are: Arandis Constituency Rural, Rehoboth East Urban Constituency Rural, Walvis Bay Rural Constituency Rural, Mpungu Constituency Urban, Etayi Constituency Urban, Kalahari Constituency Urban, and Ondobe Constituency Urban.

Mode of data collection

Face-to-face [f2f]

Research instrument

The following questionnaire instruments were used for the Namibia 2011 Population and and Housing Census:

Form A (Long Form): For conventional households and residential institutions

Form B1 (Short Form): For special population groups such as persons in transit (travellers), police cells, homeless and off-shore populations

Form B2 (Short Form): For hotels/guesthouses

Form B3 (Short Form): For foreign missions/diplomatic corps

Cleaning operations

Data editing took place at a number of stages throughout the processing, including: a) During data collection in the field b) Manual editing and coding in the office c) During data entry (Primary validation/editing) Structure checking and completeness using Structured Query Language (SQL) program d) Secondary editing: i. Imputations of variables ii. Structural checking in Census and Survey Processing System (CSPro) program

Sampling error estimates

Sampling Error The standard errors of survey estimates are needed to evaluate the precision of the survey estimation. The statistical software package such as SPSS or SAS can accurately estimate the mean and variance of estimates from the survey. SPSS or SAS software package makes use of the Taylor series approach in computing the variance.

Data appraisal

Data quality Great efforts were made to check and ensure that the Census data was of high quality to enhance its credibility and increase its usage. Various quality controls were implemented to ensure relevance, timeliness, accuracy, coherence and proper data interpretation. Other activities undertaken to enhance quality included the demarcation of the country into small enumeration areas to ensure comprehensive coverage; the development of structured Census questionnaires after consultation with government ministries, university expertise and international partners; the preparation of detailed supervisors' and enumerators' instruction manuals to guide field staff during enumeration; the undertaking of comprehensive publicity and advocacy programmes to ensure full Government support and cooperation from the general public; the testing of questionnaires and other procedures; the provision of adequate training and undertaking of intensive supervision using four supervisory layers; the editing of questionnaires at field level; establishing proper mechanisms which ensured that all completed questionnaires were properly accounted for; ensuring intensive verification, validating all information and error corrections; and developing capacity in data processing with support from the international community.

Population pharmacokinetic (PopPK) models of posaconazole have been established to promote the precision dosing. However, the performance of these models extrapolated to other centers has not been evaluated. This study aimed to conduct an external evaluation of published posaconazole PopPK models to evaluate their predictive performance. Posaconazole PopPK models screened from the PubMed and MEDLINE databases were evaluated using an external dataset of 213 trough concentration samples collected from 97 patients. Their predictive performance was evaluated by prediction-based diagnosis (prediction error), simulation-based diagnosis (visual predictive check), and Bayesian forecasting. In addition, external cohorts with and without proton pump inhibitor were used to evaluate the models respectively. Ten models suitable for the external dataset were finally included into the study. In prediction-based diagnostics, none of the models met pre-determined criteria for predictive indexes. Only M4, M6, and M10 demonstrated favorable simulations in visual predictive check. The prediction performance of M5, M7, M8, and M9 evaluated using the cohort without proton pump inhibitor showed a significant improvement compared to that evaluated using the whole cohort. Consistent with our expectations, Bayesian forecasting significantly improved the predictive per-formance of the models with two or three prior observations. In general, the applicability of these published posaconazole PopPK models extrapolated to our center was unsatisfactory. Prospective studies combined with therapeutic drug monitoring are needed to establish a PopPK model for posaconazole in the Chinese population to promote individualized dosing.

Abstract

Panel data possess several advantages over conventional cross-sectional and time-series data, including their power to isolate the effects of specific actions, treatments, and general policies often at the core of large-scale econometric development studies. While the concept of panel data alone provides the capacity for modeling the complexities of human behavior, the notion of universal panel data – in which time- and situation-driven variances leading to variations in tools, and thus results, are mitigated – can further enhance exploitation of the richness of panel information.

This Basic Information Document (BID) provides a brief overview of the Tanzania National Panel Survey (NPS), but focuses primarily on the theoretical development and application of panel data, as well as key elements of the universal panel survey instrument and datasets generated by the four rounds of the NPS. As this Basic Information Document (BID) for the UPD does not describe in detail the background, development, or use of the NPS itself, the round-specific NPS BIDs should supplement the information provided here.

The NPS Uniform Panel Dataset (UPD) consists of both survey instruments and datasets, meticulously aligned and engineered with the aim of facilitating the use of and improving access to the wealth of panel data offered by the NPS. The NPS-UPD provides a consistent and straightforward means of conducting not only user-driven analyses using convenient, standardized tools, but also for monitoring MKUKUTA, FYDP II, and other national level development indicators reported by the NPS.

The design of the NPS-UPD combines the four completed rounds of the NPS – NPS 2008/09 (R1), NPS 2010/11 (R2), NPS 2012/13 (R3), and NPS 2014/15 (R4) – into pooled, module-specific survey instruments and datasets. The panel survey instruments offer the ease of comparability over time, with modifications and variances easily identifiable as well as those aspects of the questionnaire which have remained identical and offer consistent information. By providing all module-specific data over time within compact, pooled datasets, panel datasets eliminate the need for user-generated merges between rounds and present data in a clear, logical format, increasing both the usability and comprehension of complex data.

Geographic coverage

Designed for analysis of key indicators at four primary domains of inference, namely: Dar es Salaam, other urban, rural, Zanzibar.

Analysis unit

• Households
• Individuals

Universe

The universe includes all households and individuals in Tanzania with the exception of those residing in military barracks or other institutions.

Kind of data

Sample survey data [ssd]

Sampling procedure

While the same sample of respondents was maintained over the first three rounds of the NPS, longitudinal surveys tend to suffer from bias introduced by households leaving the survey over time; i.e. attrition. Although the NPS maintains a highly successful recapture rate (roughly 96% retention at the household level), minimizing the escalation of this selection bias, a refresh of longitudinal cohorts was done for the NPS 2014/15 to ensure proper representativeness of estimates while maintaining a sufficient primary sample to maintain cohesion within panel analysis. A newly completed Population and Housing Census (PHC) in 2012, providing updated population figures along with changes in administrative boundaries, emboldened the opportunity to realign the NPS sample and abate collective bias potentially introduced through attrition.

To maintain the panel concept of the NPS, the sample design for NPS 2014/2015 consisted of a combination of the original NPS sample and a new NPS sample. A nationally representative sub-sample was selected to continue as part of the “Extended Panel” while an entirely new sample, “Refresh Panel”, was selected to represent national and sub-national domains. Similar to the sample in NPS 2008/2009, the sample design for the “Refresh Panel” allows analysis at four primary domains of inference, namely: Dar es Salaam, other urban areas on mainland Tanzania, rural mainland Tanzania, and Zanzibar. This new cohort in NPS 2014/2015 will be maintained and tracked in all future rounds between national censuses.

Mode of data collection

Face-to-face [f2f]

Research instrument

The format of the NPS-UPD survey instrument is similar to previously disseminated NPS survey instruments. Each module has a questionnaire and clearly identifies if the module collects information at the individual or household level. Within each module-specific questionnaire of the NPS-UPD survey instrument, there are five distinct sections, arranged vertically: (1) the UPD - “U” on the survey instrument, (2) R4, (3), R3, (4) R2, and (5) R1 – the latter 4 sections presenting each questionnaire in its original form at time of its respective dissemination.

The uppermost section of each module’s questionnaire (“U”) represents the model universal panel questionnaire, with questions generated from the comprehensive listing of questions across all four rounds of the NPS and codes generated from the comprehensive collection of codes. The following sections are arranged vertically by round, considering R4 as most recent. While not all rounds will have data reported for each question in the UPD and not each question will have reports for each of the UPD codes listed, the NPS-UPD survey instrument represents the visual, all-inclusive set of information collected by the NPS over time.

The four round-specific sections (R4, R3, R2, R1) are aligned with their UPD-equivalent question, visually presenting their contribution to compatibility with the UPD. Each round-specific section includes the original round-specific variable names, response codes and skip patterns (corresponding to their respective round-specific NPS data sets, and despite their variance from other rounds or from the comprehensive UPD code listing)4.

Effective conservation and management of animal populations requires knowledge of abundance and trends. For many species, these quantities are estimated using systematic visual surveys. Additional individual-level data are available for some species. Integrated population modelling (IPM) offers a mechanism for leveraging these datasets into a single estimation framework. IPMs that incorporate both population- and individual-level data have previously been developed for birds, but have rarely been applied to cetaceans. Here, we explore how IPMs can be used to improve the assessment of cetacean populations. We combined three types of data that are typically available for cetaceans of conservation concern: population-level visual survey data, individual-level capture-recapture data, and data on anthropogenic mortality. We used this IPM to estimate the population dynamics of the Cook Inlet population of beluga whales (CIBW; Delphinapterus leucas) as a case study. Our state-space IPM included a population process model and three observational submodels: 1) a group detection model to describe group size estimates from aerial survey data; 2) a capture-recapture model to describe individual photographic capture-recapture data; and 3) a Poisson regression model to describe historical hunting data. The IPM produces biologically plausible estimates of population trajectories consistent with all three datasets. The estimated population growth rate since 2000 is less than expected for a recovering population. The estimated juvenile/adult survival rate is also low compared to other cetacean populations, indicating that low survival may be impeding recovery. This work demonstrates the value of integrating various data sources to assess cetacean populations and serves as an example of how multiple, imperfect datasets can be combined to improve our understanding of a population of interest. The model framework is applicable to other cetacean populations and to other taxa for which similar data types are available.

Methods /Data/CIBW_RSideCapHist_McGuire&Stephens.csv contains a matrix of right side capture histories (1 = captured, 0 = not captured) for each individual (rows) and year (columns). Photographic capture-recapture data were collected by Tamara McGuire. These data are made available here, without restriction, but anyone wishing to use these data is requested to contact tamaracookinletbeluga@gmail.com, who can provide further information on how raw data were processed to provide capture histories.

/Data/CIBW_HuntData_Mahoney&Shelden2000.xlsx contains the minimum documented number of animals killed (MinKilled) for years between 1950 and 1998 as published in Mahoney and Shelden 2000. Entries which are NA indicate that no data were available for that year.

/Data/CIBW_Abundance_HobbsEtAl2015.xlsx contains the total group size estimates from Hobbs et al. 2015.

/Data/CIBW_Abundance_BoydEtAl2019.txt contains an array with dimensions [1:1000, 1:8, 1:11] containing 1000 posterior samples of total group size for up to 8 survey days over 11 years, as described in Boyd et al. 2019.

Abstract

The Health and Demographic Surveillance System (HDSS) in Niakhar, a rural area of Senegal, is located 135 km east of Dakar. This HDSS has been set up in 1962 by the Institut de Recherche pour le Développement (IRD) to face the shortcomings of the civil registration system and provide demographic indicators.

Some 65 villages were followed annually in the Niakhar area from 1962 to 1969. The study zone was reduced to eight villages from 1969 to 1983, and from then on the HDSS was extended to include 22 other villages, covering a total of 30 villages for a population estimated at 45,000 in December 2013. Thus 8 villages have been under demographic surveillance for almost 50 years and 30 villages for 30years.

Vital events, migrations, marital changes, pregnancies, immunization are routinely recorded (every four months). The database also includes epidemiological, economic and environmental information coming from specific surveys. Data were collected through annual rounds from 1962 to 1987; rounds became weekly from 1987 to 1997; routine visits were conducted every three months between 1997and 2007 and every four months since then.

The current objectives are 1) to obtain a long-term assessment of demographic and socio-economic indicators necessary for bio-medical and social sciences research, 2) to keep up epidemiological and environmental monitoring, 3) to provide a research platform for clinical and interdisciplinary research (medical, social and environmental sciences). Research projects during the last 5 years are listed in Table 2. The Niakhar HDSS has institutional affiliation with the Institut de Recherche pour le Développement (IRD, formerly ORSTOM).

Geographic coverage

The study zone of Niakhar is located in Senegal, 14.5ºN Latitude and 16.5ºW Longitude in the department of Fatick (Sine-Saloum), 135 km east of Dakar. The Niakhar study zone covers 203 square kilometres and is located in the continental Sahelian-Sudanese climatic zone. For thirty years the region has suffered from drought. The average annual rainfall has decreased from 800 mm in the 1950s to 500 mm in the 1980s. Increasing amounts of precipitation have been observed since the mid-2000s with an average annual rainfall of 600 mm between 2005 and 2010. The area is 203 square kilometers.

Analysis unit

Individual

Universe

Members of households reside within the demographic surveillance area. Inmigrants are defined by intention to become resident, but actual residence episodes of less than 180 days are censored. Outmigrants are defined by intention to become resident elsewhere, but actual periods of non-residence less than 180 days are censored, except seasonal work migrants, worker with a wife resident, pupils or students. Children born to resident women are considered resident by default, irrespective of actual place of birth. The dataset contains the events of all individuals ever resident during the study period (1 Jan 1990 to 31 Dec 2013).

The Niakhar HDSS collects for each resident the following basic data: individual, household and compound identifying information, mother and father identification, relationship to the head of household and spousal relationship. From 1983 to 2007, the HDSS routinely monitored deaths, pregnancies, births, miscarriages, stillbirths, weaning, migrations, changes of marital status, immunizations, and cases of measles and whooping cough. For the last 5 years, the HDSS only recorded demographic events related to each resident including cause of death. Verbal autopsies have been conducted after all deaths except for those that occurred between 1999 and 2004 where only deaths for people aged 0-55 years were investigated. The Niakhar HDSS also registers visitors as well as all the demographic events related to them in case of in-migration. Household characteristics (living conditions, domestic equipment, etc.) were collected in 1998 and 2003, and community equipment (schools, boreholes, etc.) in 2003. Economic and environmental data will be collected in 2013. Table 3 presents further details on the data items collected. The Niakhar HDSS interviewers collect data with tablet PCs that are loaded with the last updated database linked to a user-friendly interface indicating the household members and the questionnaire. Daily backups are performed on an external hard drive and weekly synchronizations are scheduled during the round, helping to update the database and check data consistency (i.e. residential moves within the study area or marriages). Applications are Developed in Visual Basic.Net and the database is managed with Microsoft Access.

Kind of data

Event history data

Frequency of data collection

This dataset contains rounds 1 to 18 of demographic surveillance data covering the period from 1 Jan 1983 to 31 December 2015.

From 1983 to 1987, data were collected through annual rounds during the dry season. Demographic events were collected by interviewers using a printed list of compound residents with their characteristics. From 1987 to 1997, rounds became weekly because of the need for continuous birth registration for vaccine trials. Annual censuses were carried out to check data collection, particularly relative to in- and out-migration. Routine visits were conducted in the 30 villages of the study area every three months between 1997and 2007 and every four months between 2008 and 2012 and every six month since then.

Sampling procedure

This dataset is not based on a sample; it contains information from the complete demographic surveillence area.

Sampling deviation

None

Mode of data collection

Proxy Respondent [proxy]

Research instrument

List of questionnaires:

Compound Registration or update Form Houshold Registration or update Form Household Membership Registration or update Form External Migration Registration Form Internal Migration Registration Form Individual Registration Form Birth Registration Form Death Registration Form

Cleaning operations

On data entry data consistency and plausibility were checked by 455 data validation rules at database level. If data validaton failure was due to a data collection error, the questionnaire was referred back to the field for revisit and correction. If the error was due to data inconsistencies that could not be directly traced to a data collection error, the record was referred to the data quality team under the supervision of the senior database scientist. This could request further field level investigation by a team of trackers or could correct the inconsistency directly at database level.

No imputations were done on the resulting micro data set, except for:

a. If an out-migration (OMG) event is followed by a homestead entry event (ENT) and the gap between OMG event and ENT event is greater than 180 days, the ENT event was changed to an in-migration event (IMG). b. If an out-migration (OMG) event is followed by a homestead entry event (ENT) and the gap between OMG event and ENT event is less than 180 days, the OMG event was changed to an homestead exit event (EXT) and the ENT event date changed to the day following the original OMG event. c. If a homestead exit event (EXT) is followed by an in-migration event (IMG) and the gap between the EXT event and the IMG event is greater than 180 days, the EXT event was changed to an out-migration event (OMG). d. If a homestead exit event (EXT) is followed by an in-migration event (IMG) and the gap between the EXT event and the IMG event is less than 180 days, the IMG event was changed to an homestead entry event (ENT) with a date equal to the day following the EXT event. e. If the last recorded event for an individual is homestead exit (EXT) and this event is more than 180 days prior to the end of the surveillance period, then the EXT event is changed to an out-migration event (OMG)

In the case of the village that was added (enumerated) in 2006, some individuals may have outmigrated from the original surveillance area and setlled in the the new village prior to the first enumeration. Where the records of such individuals have been linked, and indivdiual can legitmately have and outmigration event (OMG) forllowed by and enumeration event (ENU). In a few cases a homestead exit event (EXT) was followed by an enumeration event in these cases. In these instances the EXT events were changed to an out-migration event (OMG).

Response rate

On an average the response rate is about 99% over the years for each round

Sampling error estimates

Not Applicable

Data appraisal

CentreId MetricTable QMetric Illegal Legal Total Metric RunDate SN013 MicroDataCleaned Starts 86883 2017-05-19 15:12
SN013 MicroDataCleaned Transitions 241970 241970 0 2017-05-19 15:12
SN013 MicroDataCleaned Ends 86883 2017-05-19 15:12
SN013 MicroDataCleaned SexValues 32 241938 241970 0 2017-05-19 15:12
SN013 MicroDataCleaned DoBValues 241970 2017-05-19 15:12

The survey focused on examining the relationship of the population of the Slovak Republic to visual and applied arts. The introductory question studied the intensity of interest in each type of art based on selected criteria. Respondents were then asked to state which of the three areas of visual and applied arts is the closest to them. The survey also examined the greatest stimulus of interest in visual arts, visits to the nearest facility presenting visual arts, and the overall frequency of attendance at visual arts exhibitions. Another set of questions measured literacy in visual arts. Respondents were given a list of significant visual arts events in Slovakia and were asked which of them they knew and attended. They were also instructed to name at least three book illustrators, match works of visual arts with their creators, and indicate which important buildings or galleries in Slovakia and abroad they have visited. The survey was carried out via a network of interviewers on a sample of 1131 respondents (the population of the Slovak Republic above 18 years of age) using the quota characteristics of gender, age, education, the size of the settlement, nationality, district and region as well as economic status, religious denomination, social class (upper, upper middle, middle, lower middle, lower class) and political orientation (left, right, middle, etc.). Standardized interview using a questionnaire Adult inhabitants of Slovakia (18+) Quota sampling

Dataset

Dataset accompanying the manuscript "Feedback contribution to surface motion perception in the human early visual cortex" (biorxiv).

Description

fMRI data are arrange by subject (following BIDS convention). For each subject, there are subfolders for anatomical and functional MRI data.

├── sub-01 │ ├── anat │ │ └── ... │ ├── func │ │ └── ... │ ├── func_se │ │ └── ... │ └── func_se_op │ └── ...

The subfolder 'anat' contains four images from the MP2RAGE sequence (among these, T1 and proton-density weighted images). The subfolder 'func' contains the functional data (GE EPI, T2* weighted) from the main experiment (i.e. the data from which the haemodynamic response was estimated, and on which statistical analysis was performed). The subfolders 'func_se' and 'func_se_op' contain SE EPI images with opposite phase encode polarity that were used for distortion correction. Moreover, for each image/timeseries there is a json file with metadata.

Anatomical images have been masked anteriorly (defaced). Functional images are in coronal oblique orientation, covering early visual cortex.

The folder 'stimuli' contains information on the stimuli used for retinotopic mapping, including timecourse models used for population receptive field mapping. (These files are included here because of their relatively large file size, which would make distribution via a git repository impractical.) The software used for the presentation of retinotopic mapping stimuli (and for the corresponding analysis) is available on github.

For example videos of the main experimental stimuli, see zenodo.2583017. If you would like to reproduce the experimental stimuli, the respective PsychoPy code can be found on github.

The exact timing of events during the experiments (rest & stimulus blocks, target events) can be found in FSL-style design matrices ("3 column format") on github.com/ingo-m/PacMan/tree/master/analysis/FSL_MRI_Metadata.

Analysis

The analysis pipeline makes use of several MRI software packages (such as SPM and FSL for preprocessing, and CBS tools for cortical depth sampling). In order to facilitate reproducibility, the entire analysis was containerised using docker. Because of licensing issues, the docker images with the third-party software cannot be directly made available. However, the docker files and detailed instructions for the creation of the docker images are available on github.

If you would like to reproduce the analysis, the first step will be to create the docker images (which provide an exact copy of the system environment that was used to conduct the published analysis). There are two docker images, one for the main analysis (motion correction, distortion correction, GLM fitting; named "dockerimage_pacman_jessie"), and another one for the depth sampling (named "dockerimage_cbs"). Detailed instructions on how to create the docker images can be found here and here.

Once you set up the docker images, the analysis can be run automatically. For each subject, there is one parent script for the main analysis (e.g. ~/analysis/20180118/metascript_01.sh for subject 20180118) and a separate script for the depth sampling (e.g. ~/analysis/20180118/metascript_03.sh). The only manual adjustments you should have to perform to reproduce the analysis is to change the file paths in the first section of these scripts ('pacman_anly_path' is the parent directory containing the analysis code, i.e. the git repository, and 'pacman_data_path' is the parent directory containing the MRI data). The main analysis (metascript_01.sh) should take about 24 h per subject on a workstation with 12 cores, and the depth sampling (metascript_02.sh) about 2 h. The analysis can be run on consumer-grade hardware, but some parts of the analysis may not run with less than 16 GB of RAM (recommended: 32 GB).

Visualisations (e.g. cortical depth profiles and signal timecourses) and group-level statistical tests are implemented in py_depthsampling.

Further resources

Please refer to the research paper for more details: https://doi.org/10.1101/653626

The analysis pipeline can be found on https://github.com/ingo-m/PacMan

A separate repository contains the code used for visualisation of depth-sampling results: https://github.com/ingo-m/py_depthsampling/tree/PacMan

Free & open source software package for population receptive field mapping: https://github.com/ingo-m/pyprf

2014 - 2015. This dataset is a de-identified summary table of vision and eye health data indicators from ACS, stratified by all available combinations of age group, race/ethnicity, gender, and state. ACS is an annual nationwide survey conducted by the U.S. Census Bureau that collects information on demographic, social, economic, and housing characteristics of the U.S. population. Approximate sample size is 3 million annually. ACS data for VEHSS includes one question related to Visual Function. Data were suppressed for cell sizes less than 30 persons, or where the relative standard error more than 30% of the mean. Data will be updated as it becomes available. Detailed information on VEHSS ACS analyses can be found on the VEHSS ACS webpage (link). Additional information about ACS can be found on the U.S. Census Bureau website (https://www.census.gov/content/dam/Census/programs-surveys/acs/about/ACS_Information_Guide.pdf). The VEHSS ACS dataset was last updated in June 2018.

Context

Predicting wildfire spread is critical for land management and disaster preparedness. To this end, we present `Next Day Wildfire Spread,' a curated, large-scale, multivariate data set of historical wildfires aggregating nearly a decade of remote-sensing data across the United States. In contrast to existing fire data sets based on Earth observation satellites, this data set combines 2D fire data with many explanatory variables (e.g., topography, vegetation, weather, drought index, population density) aligned over 2D regions, providing a feature-rich data set for machine learning applications. This data set can be used as a benchmark for developing wildfire propagation models based on remote sensing data for a lead time of one day.

Content

We aggregate the data across the contiguous United States from 2012 to 2020. The data set has a total of 18,445 samples. Each sample is a 64 km x 64 km region at 1 km resolution from a location and time at which a fire occurred. We represent the fire information as a fire mask over each region, showing the locations of ‘fire’ versus ‘no fire’, with an additional class for uncertain labels (i.e., cloud coverage or other unprocessed data). To capture the fire spreading pattern, we include both the fire mask at time t (which we call ‘previous fire mask’) and at time t + 1 day (which we call ‘fire mask’). Using Google Earth Engine (GEE), we aggregate data from different data sources and overlay the fire data in location and time with other variables relevant to wildfire predictions. In addition to the fire data, this data set contains the following features: elevation, wind direction and wind speed, minimum and maximum temperatures, humidity, precipitation, drought index, normalized difference vegetation index (NDVI), energy release component (ERC), and population density.

The following figure shows examples from this data set. In the fire masks, red corresponds to fire, while grey corresponds to no fire. Black indicates uncertain labels (i.e., cloud coverage or other unprocessed data).

https://i.postimg.cc/bYxHNVVV/data-visualization.png" alt="data-visualization.png">

The published notebook provides an example of how to read and plot the data.

A detailed description of this data set is provided here: Arxiv paper.

Data Source

• The historical wildfire data are from the MOD14A1 V6 data set [1].
• Topography data are from the Shuttle Radar Topography Mission (SRTM) [2].
• Weather data are from the Gridded Surface Meteorological data set (GRIDMET) [3].
• Drought data are from the GRIDMET Drought data set [4].
• Vegetation data are from the NASA VIIRS Vegetation Indices (VNP13A1) data set [5].
• Population density data are from the Gridded Population of World Version 4 (GPWv4) data set [6].

Inspiration

Some potential questions that this data set can be used to answer include: - Given a fire on a given day, where will the fire spread the following day? - What are the main variables related to fire spreading?

Citation

[1] L. Giglio and C. Justice, “Mod14a1 modis/terra thermal anomalies/fire daily l3 global 1km sin grid v006,” 2015, https://doi.org/10.5067/MODIS/MOD14A1.006.

[2] T. G. Farr, P. A. Rosen, E. Caro, R. Crippen, R. Duren, S. Hensley, M. Kobrick, M. Paller, E. Rodriguez, L. Roth, D. Seal, S. Shaffer, J. Shimada, J. Umland, M. Werner, M. Oskin, D. Burbank, and D. Alsdorf, “The shuttle radar topography mission,” Reviews of Geophysics, vol. 45, no. 2, 2007, https://doi.org/10.1029/2005RG000183.

[3] J. T. Abatzoglou, “Development of gridded surface meteorological data for ecological applications and modelling,” International Journal of Climatology, vol. 33, no. 1, pp. 121–131, 2013, https://doi.org/10.1002/joc.3413.

[4] J. T. Abatzoglou, D. E. Rupp, and P. W. Mote, “Seasonal climate variability and change in the pacific northwest of the united states,” Journal of Climate, vol. 27, no. 5, pp. 2125–2142, 2014, https://doi.org/10.1002/joc.3413.

[5] K. Didan and A. Barreto, “Viirs/npp vegetation indices 16-day l3 global 500m sin grid v001,” 2018, [https://doi.o...

Communal bird roosts serve as information centers and a means of thermoregulation for many species. While some communally roosting species are major pests and cause dis-amenities, others are of conservation concern. Estimating the population of roosting birds can provide a useful proxy of population size and possibly a more reliable estimate than other sampling techniques. However, estimating these populations is challenging as some roosts are large and often occluded in foliage. Previous acoustic methods such as paired sampling, microphone arrays, and use of call rate have been used to estimate bird abundances; however, these are less suited for estimating large roost populations where hundreds of individuals are calling in unison. To address this challenge, we explored using machine learning techniques to estimate a roost population of the Javan Myna, Acridotheres javanicus, an invasive species in Singapore. While one may expect to use sound intensity to estimate roost sizes, it is affected by various factors such as the distance of the recorder, local propagation conditions (e.g., buildings and trees), weather conditions, and noise from other sources. Here, we used a deep neural network to extract higher-order statistics from the sound recordings and use those to help estimate roost sizes. Additionally, we validated our method using automated visual analysis with a dual-camera setup and manual bird counts. Our estimated bird counts over time using our acoustic model matched the automated visual estimates and manual bird counts at a selected Javan Myna roost, thus validating our approach. Our acoustic model estimated close to 400 individual mynas roosting in a single tree. Analyses of additional recordings of Javan Myna roosts conducted on two separate occasions and at a different roost location using our acoustic model showed that our roost estimates over time also matched our automated visual estimates well. Our novel approach to estimating communal roost sizes can be achieved robustly using a simple portable acoustic recording system. Our method has multiple applications such as testing the efficacy of avian roost population control measures (e.g., roost tree pruning) and monitoring the populations of threatened bird species that roost communally. Methods 1. Video analysis technique To develop an acoustic technique to estimate myna roost sizes, we had to ground truth the number of roosting mynas to calibrate our acoustic model. To achieve this, we developed an automated visual technique to count mynas flying in and out of a roost site early in the evenings, before mynas start arriving at the site. The difference between the cumulative number of mynas arriving at the site and leaving the site is the number of mynas at the roost site. While manually counting mynas coming in and out of a roost site is possible, it is error-prone and labour-intensive. We therefore focused on the development of an automated visual technique based on the analysis of video recordings from two cameras pointed at the roost tree from different angles. The two cameras together provided a full view of the tree from all angles, so all arriving and leaving mynas could be counted. Additionally, we validated the technique by manually counting the mynas in one set of video recordings and compared it against the visual automated analysis. We chose a roost tree that was separated from nearby trees (i.e., non-joining canopies) and other nearby occlusions, such that we could see mynas coming in and out of the tree from all directions. Two cameras were deployed facing the tree from about 1-2 hours before sunset, until well after sunset. This covers the time during which the mynas arrive at the tree, and lasts until the end of the acoustic measurements that we compare the camera's counts against. Camera 1 was usually set up on the south-east of the tree, and Camera 2 on the north-west. We collected multiple datasets from the same roost site on different days, at different times of the year (Table 1). To automate our detection of birds flying in and out of the roost site, we drew boundaries around the tree and counted birds crossing the boundaries in either direction. We call these boundaries “virtual markers”. Whenever a bird crossed a marker, we estimated its direction of flight, determined if it was flying in or out, and updated the estimated bird count at the roost site. Detecting dark birds against a light sky background (even in twilight hours with sufficient light) was reliably achieved with simple image processing techniques. We used a rapid change in the brightness of pixels on the marker for bird detection. We added a minimum required time gap between detections in the same location in the image to avoid duplicate detections from the same bird flapping its wings or moving in a way that causes the brightness to oscillate as the bird crosses the marker. While two cameras ensured that we had a complete view of the roost tree to see birds arriving from all directions, it also posed a challenge. A single bird might be seen on both cameras and could be double-counted. Birds crossing the marker from the south-west or north-east could be potentially detected on both cameras. To avoid double-counting, we had to associate detections from both cameras and only count detections on one of the cameras. This was achieved with heuristics such as proximity in time, detection of opposite boundaries on the two cameras, and direction of flight. The dataset collected on 3 September 2020 was used as the primary dataset for validation of the visual analysis technique (Table 1). For this dataset, we performed manual counting of birds by carefully watching videos from both cameras and annotating the arrival and departure of each bird. 2. Acoustic recording analysis technique The audio dataset collected on 3 September 2020 was used as training data in our acoustic analysis. An acoustic recorder was set up close to Camera 1 during data collection. The exact locations of the cameras and the acoustic recorder differed on different days, as the intent was to make the techniques robust against small differences in the recorder setup. Both cameras and the acoustic recorder were synchronised in time. The audio data was collected using a Zoom H6 recorder and an Electro-Voice ND66 condenser cardioid instrument microphone. The directional microphone was mounted on a tripod and placed about 5–10 m from the roost tree of interest and pointed into the centre of the foliage of the tree. The acoustic technique developed is not sensitive to the exact distance, as long as the roost chorus is audible at the microphone and the roost does not span more than a 90° angle from the microphone. The microphone has a beamwidth of about 90°, which was sufficient to cover the roost site, but not so wide as to pick up significant noise from other nearby roost sites. In the time series of the recorded data, the sound intensity increased as the roost chorus got louder through the evening. The sudden drop in intensity at the 1 hour 24-minute mark occurred during a disturbance, and then gradually increased as the birds returned to their roost. After sunset, the roost chorus gradually fades till the birds stop vocalizing. Several loud events also can be observed throughout the recording, representing noises that are inevitable when recording in uncontrolled settings and public places. While the data at first glance suggested we could use the acoustic time series amplitude to estimate roost sizes, it can be confounded by multiple factors. These include the distance between the roost site and the recorder, the environmental acoustic propagation conditions, the local noise sources, the gain settings on the recorder, and the pointing direction of the microphone. Thus, the time series amplitude might not represent a close proxy of roost size since these variables were difficult to control operationally. As such, we considered other properties of the acoustic time series in our analysis. 3. Machine learning A traditional approach to finding acoustic time series properties of interest would be to handcraft features based on temporal statistics of the time series data. Such features often include ratios of power spectral densities at various frequencies, and other higher-order temporal statistics. These handcrafted features can then be used for regression analysis to calibrate a model. Here, we applied a deep neural network (DNN) to learn the features from the time series data. Before feeding the time series data to a DNN, we decided to bandpass filter the data to remove frequencies that were dominated by traffic and other urban sounds and did not contain much roost chorus. Since the roost chorus was mostly in the 1–5 kHz band, we applied a digital finite impulse response (FIR) bandpass filter (with 128 taps) to remove other sounds. The recording was then down-sampled at 16384 Hz, well above Nyquist frequency, to reduce the number of time series samples in the recording. The recorded time series was then split into 4096 sample blocks (250 ms blocks) and used as input to the DNN. We used a 1D convolutional DNN with three convolutional layers, one mean pooling layer, followed by three dense fully connected layers in the DNN, working directly with the acoustic data at the input. This is quite different from common approaches in DNN, where the data is first converted to a 2D spectrogram image and fed to a 2D convolutional DNN designed to work with images. Here, the 2D spectrogram conversion was unnecessary, and potentially detrimental to the retention of information in the acoustic recording as spectrogram conversion loses phase information from the original time series data. We used a normalization layer at the input of the DNN, removing any cues on acoustic intensity, as we did not want the DNN to learn to use the relationship with intensity for roost size estimation. While the relationship

PURPOSE: The SCUBA survey was designed to assess the density of small lobsters (1-3 years of age) in rocky reefs, in the nearshore habitat. DESCRIPTION: Total number of transects surveyed and total number of lobsters measured for each site in each year. There are some sites that do not have any coordinates identified, therefore these have not been included in the Web Map Services (WMS). PARAMETERS COLLECTED: size measurement (biological); species counts (ecological); substrate (geological) SAMPLING METHODS: Transects are laid-out from a small vessel using buoys, anchors, and a 100 m leaded rope along the bottom, marked at 5 m intervals. A strip transect survey method is used whereas two divers sample a 1 or 2 m strip (dependent on lobster density) alongside either side of the leaded rope. All captured lobster are measured (carapace length) and all lobsters of ≥20 mm carapace length are sexed. The complexity and suitability of the habitat is assessed in the 5 m sections (e.g. rocky reefs, sand, large boulders). USE LIMITATION: To ensure scientific integrity and appropriate use of the data, we would encourage you to contact the data custodian.

Haloptilus longicornis population structure (Atlantic Ocean) - Microsatellite data. access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv acquisition_description=Refer to the following publication for complete methodology details:

Goetze, E.,\u00a0Andrews, K., Peijnenburg, K. T. C. A., Portner, E., Norton, E. L. (2015) Temporal Stability of Genetic Structure in a Mesopelagic Copepod.\u00a0\u00a0PLoS One\u00a010(8): e0136087.\u00a0doi:10.1371/journal.pone.0136087

In summary (excerpted from above):

For\u00a0H.\u00a0longicornis species 1, deviations from Hardy-Weinberg equilibrium (HWE) and linkage disequilibrium were examined using ARLEQUIN v3.5.1.3 and GENEPOP v4.2 for all microsatellite loci [36\u201338]. We tested for the presence of null alleles in microsatellite data using MICROCHECKER v2.2.3 [39], and estimated null allele frequencies and calculated population pairwise\u00a0FST\u00a0values with correction for null alleles in FreeNA [40]. Microsatellite genetic diversity indices of observed and expected heterozygosity, average alleles per locus, and allele richness were calculated in GENETIX v4.05 and FSTAT [35,41]. Pairwise\u00a0FST\u00a0values were calculated among all sample sites using both microsatellite and mtCOII data, as a measure of population subdivision across samples (ARLEQUIN v3.5.1.3, [38]). Significance was assessed following correction for multiple comparisons using the false discovery rate (FDR, [42,43]). Pairwise \u03a6ST\u00a0values also were calculated for the mtCOII data. We identified the nucleotide substitution model that best fit our mtCOII data using the Akaike Information Criterion, as implemented in jModelTest v2.1.4 [44], and the K81 or three- parameter model was selected as the best model (TPM3uf+G). The Tamura and Nei substitution model, which was the closest available model in Arlequin, was used to calculate pairwise and global \u03a6ST\u00a0values, and to estimate genetic diversity at each site. Hierarchical Analyses of Molecular Variance (AMOVA) based on\u00a0FST\u00a0were carried out to partition the genetic variance across both space (ocean gyres) and time (sampling years), for both marker types. In these analyses, we tested for population structure under the following groupings: with samples stratified by (1) northern and southern subtropical gyres (2 gyres), and (2) across two sampling years (2010, 2012). Global\u00a0FST\u00a0values were estimated using non-hierarchical AMOVAs among all samples, as well as among subsets of the data across ocean gyres and sampling years. Significance was tested with 10,000 permutations of genotypes or haplotypes among populations. Principal coordinate analysis (PCA) plots of linearized pairwise\u00a0FST\u00a0values based on both mtCOII and microsatellite data were used to visualize spatial and temporal genetic differentiation among samples. Population structure was further examined using a Bayesian clustering method implemented in STRUCTURE [45,46] for microsatellite loci. We used admixture and correlated allele frequency models, with a burn-in of 105\u00a0steps followed by 106\u00a0steps, with and without using sampling location as a prior. We ran these analyses for each of the 2010 and 2012 datasets using\u00a0K\u00a0= 1 to\u00a0K\u00a0= 10, and for the dataset of combined years using\u00a0K\u00a0= 1 to\u00a0K\u00a0= 20. We ran three separate replicates for each K to investigate consistency of Pr(X|K). The true\u00a0K\u00a0was evaluated by visual inspection of barplots and comparing Pr(X|K) across\u00a0K\u00a0values. awards_0_award_nid=537990 awards_0_award_number=OCE-1338959 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1338959 awards_0_funder_name=NSF Division of Ocean Sciences awards_0_funding_acronym=NSF OCE awards_0_funding_source_nid=355 awards_0_program_manager=David L. Garrison awards_0_program_manager_nid=50534 awards_1_award_nid=539716 awards_1_award_number=OCE-1029478 awards_1_data_url=http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1029478 awards_1_funder_name=NSF Division of Ocean Sciences awards_1_funding_acronym=NSF OCE awards_1_funding_source_nid=355 awards_1_program_manager=David L. Garrison awards_1_program_manager_nid=50534 cdm_data_type=Other comment=Haloptilus longicorns population structure Erica Goetze, PI Version 20 March 2017 Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/bco-dmo.699458.1 infoUrl=https://www.bco-dmo.org/dataset/699458 institution=BCO-DMO instruments_0_acronym=Thermal Cycler instruments_0_dataset_instrument_description=PCR products were genotyped instruments_0_dataset_instrument_nid=699475 instruments_0_description=General term for a laboratory apparatus commonly used for performing polymerase chain reaction (PCR). The device has a thermal block with holes where tubes with the PCR reaction mixtures can be inserted. The cycler then raises and lowers the temperature of the block in discrete, pre-programmed steps.

(adapted from http://serc.carleton.edu/microbelife/research_methods/genomics/pcr.html) instruments_0_instrument_name=PCR Thermal Cycler instruments_0_instrument_nid=471582 instruments_0_supplied_name=ABI3730 Genetic Analyzer metadata_source=https://www.bco-dmo.org/api/dataset/699458 param_mapping={'699458': {}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/699458/parameters people_0_affiliation=University of Hawaii at Manoa people_0_affiliation_acronym=SOEST people_0_person_name=Erica Goetze people_0_person_nid=473048 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=Woods Hole Oceanographic Institution people_1_affiliation_acronym=WHOI BCO-DMO people_1_person_name=Hannah Ake people_1_person_nid=650173 people_1_role=BCO-DMO Data Manager people_1_role_type=related project=Plankton Population Genetics,Plankton_PopStructure projects_0_acronym=Plankton Population Genetics projects_0_description=Description from NSF award abstract: Marine zooplankton show strong ecological responses to climate change, but little is known about their capacity for evolutionary response. Many authors have assumed that the evolutionary potential of zooplankton is limited. However, recent studies provide circumstantial evidence for the idea that selection is a dominant evolutionary force acting on these species, and that genetic isolation can be achieved at regional spatial scales in pelagic habitats. This RAPID project will take advantage of a unique opportunity for basin-scale transect sampling through participation in the Atlantic Meridional Transect (AMT) cruise in 2014. The cruise will traverse more than 90 degrees of latitude in the Atlantic Ocean and include boreal-temperate, subtropical and tropical waters. Zooplankton samples will be collected along the transect, and mitochondrial and microsatellite markers will be used to identify the geographic location of strong genetic breaks within three copepod species. Bayesian and coalescent analytical techniques will test if these regions act as dispersal barriers. The physiological condition of animals collected in distinct ocean habitats will be assessed by measurements of egg production (at sea) as well as body size (condition index), dry weight, and carbon and nitrogen content. The PI will test the prediction that ocean regions that serve as dispersal barriers for marine holoplankton are areas of poor-quality habitat for the target species, and that this is a dominant mechanism driving population genetic structure in oceanic zooplankton. Note: This project is funded by an NSF RAPID award. This RAPID grant supported the shiptime costs, and all the sampling reported in the AMT24 zooplankton ecology cruise report (PDF). Online science outreach blog at: https://atlanticplankton.wordpress.com projects_0_end_date=2015-11 projects_0_geolocation=Atlantic Ocean, 46 N - 46 S projects_0_name=Basin-scale genetics of marine zooplankton projects_0_project_nid=537991 projects_0_start_date=2013-12 projects_1_acronym=Plankton_PopStructure projects_1_description=Description from NSF award abstract: This research will test whether habitat depth specialization is a primary trait driving large-scale population genetic structure in open ocean zooplankton species. Very little is known about population connectivity in marine zooplankton. Although zooplankton were long thought to be high-gene-flow systems with little genetic differentiation among populations, recent observations have challenged this view. In fact, zooplankton species may be genetically subdivided at macrogeographic, regional, or even smaller spatial scales. Recent studies also indicate that subtle, species-specific ecological factors play an important role in controlling gene flow among plankton populations. The investigator hypothesizes that depth-related habitat, including diel vertical migration (DVM) behavior, plays a critical role in controlling dispersal of plankton among ocean regions, through interactions with ocean circulation and bathymetry. This study will compare the population genetic structures of eight planktonic copepods that utilize different depth-related habitats, in order to test key predictions of genetic structure based on the interaction of organismal depth with the oceanographic environment. The objectives of the research are to: 1) Develop novel nuclear markers that can be used to resolve genetic structure and estimate gene flow among copepod populations, 2) Characterize the spatial patterns of gene flow among populations in distinct ocean regions of the Atlantic, Pacific, and Indian Oceans for eight target species using a multilocus approach, and 3) Test the central hypothesis that depth-related habitat will significantly impact the extent of genetic structure both across and within ocean basins, the magnitude and direction of

This project was initiated by the CDFW Central Region and was conducted on a portion of the Tuolumne herd that migrate to the Jawbone Ridge flats in the winter in Tuolumne County, Mariposa County, and Alpine County. Jawbone Ridge and the adjacent winter range habitat was further divided into the Clavey and Cherry sub-herd units. Additionally, a small sample of deer were captured from the Yosemite herd (south of the Tuolumne herd) to determine herd overlap. The raw dataset consisted of GPS way points collected from Advanced Telemetry Solutions (ATS) store on board GPS collars (G2110B/D model) and were placed on female mule deer only. Individuals were captured via darting or clover traps. This data was collected from 2009-2015 by Nathan Graveline and Ronald Anderson. GPS collars were set to take a location every 7 hours, and emit a signal Monday through Friday, 9am to 5pm. Some GPS collars were set to take a location fix every hour during periods of time when deer were thought to be migrating (May and November). The Clavey and Cherry sub-herd units support the highest concentration of wintering deer within the Tuolumne deer herd range. The majority of deer in these two sub-herds migrate east into the Emigrant and Yosemite Wilderness, with a few heading north to the Carson-Iceberg Wilderness. Low density populations of non-migratory deer are present in the winter range. Forest practices, wildfires, and recreation (hunting, camping, OHV) represent the most significant impacts to this herd. To improve the quality of the data set as per Bjørneraas et al. (2010), theGPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual deer is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. The methodology used for this migration analysis allowed for the mapping of winter ranges and the identification and prioritization of migration corridors in a single deer population. Brownian Bridge Movement Models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 83 deer, including location, date, time, and average location error as inputs in Migration Mapper. 245 migration sequences were used in the modeling analysis. Corridors and stopovers were prioritized based on the number of animals moving through a particular area. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Due to varying fix rates, separate models using Brownian bridge movement models (BMMM) and fixed motion variances of 1000 were produced per migration sequence and visually compared for the entire dataset, with best models being combined prior to population-level analyses (25% of sequences selected with BMMM). Migration corridors, stopovers, and winter range analyses were produced separately for the Yosemite Herd sample (n = 6) and merged with the Tuolumne dataset given the smaller capture effort and intention to prioritize moderate and high use corridors specifically in the Tuolumne herd. Winter range analyses were based on data from 85 individual deer in total. A separate BBMM was created for all deer locations designated as winter range using a fixed motion variance parameter of 1000. Winter range designations for this herd would likely expand with a larger sample south of Jawbone Ridge (Yosemite Herd) due to a small capture sample size from this area, filling in some of the gaps between winter range polygons in the map. Large water bodies were clipped from the final outputs.Corridors are visualized based on deer use per cell in the BBMMs, with greater than or equal to 1 deer, greater than or equal to 9 deer (10% of the sample), and greater than or equal to 17 deer (20% of the sample) representing migration corridors, moderate use, and high use corridors, respectively. Stopovers were calculated as the top 10 percent of the population level utilization distribution during migrations and can be interpreted as high use areas. Stopover polygon areas less than 20,000 m2were removed, but remaining small stopovers may be interpreted as short-term resting sites, likely based on a small concentration of points from an individual animal. Winter range is visualized as the 50thpercentile contour of the winter range utilization distribution.

Mental well-being is more than merely the absence of mental illness; it is a multidimensional concept that includes both emotional and functional well-being, which are valuable resources during adolescence. In order to develop relevant interventions and policies to strengthen adolescent mental health, a continuous monitoring of the population well-being becomes important. The aim of the study was to examine the level, distribution, and changes in mental well-being over time in a Swedish adolescent population. Current study is based on four waves (2014-2017-2020-2023) of a cross-sectional student survey (N = 16288, Mage = 16.23). The outcome was measured with the Mental Health Continuum Short Form. Ten explanatory factors were chosen to examine differences in mental well-being in the study population: Grade, Sex, Sexual orientation, Socioeconomic status, Country of birth, Visual, Hearing or Mobility impairment, Specific learning disorder and Neurodevelopmental disorder. Differences in mental well-being between groups as well as temporal trends were examined and evaluated through statistical testing and hierarchical multiple linear regressions modeling. Girls, non-heterosexual adolescents, and adolescents with low socioeconomic status or impairments have lower levels of mental well-being than boys, heterosexual adolescents, and adolescents with higher socioeconomic status or without impairments, respectively. A deterioration in mental well-being is seen over time for several groups; however, results of the multivariable analysis indicates that the deterioration is mainly an effect of sex and the significant decline in mental well-being seen among girls. The most significant factor for explaining the variation in mental well-being in this study is socioeconomic status. This study elucidates temporal changes and differences in levels of mental well-being between social groups in the adolescent population. The overall differences are small, but their potential implications for public health warrant careful consideration since they concern a significant part of the population. The results underscore the imperative of promoting mental well-being in adolescents, particularly among vulnerable groups.

Abstract

The multi-country Study on Global Ageing and Adult Health (SAGE) is run by the World Health Organization's Multi-Country Studies unit in the Health Systems and Innovation Cluster. SAGE is part of the unit's Longitudinal Study Programme which is compiling longitudinal data on the health and well-being of adult populations, and the ageing process, through primary data collection and secondary data analysis. SAGE baseline data (Wave 0, 2002/3) was collected as part of WHO's World Health Survey http://www.who.int/healthinfo/survey/en/index.html (WHS). SAGE Wave 2 (2014/15) provides a comprehensive data set on the health and well-being of adults in six low and middle-income countries: China, Ghana, India, Mexico, Russian Federation and South Africa.

Objectives: To obtain reliable, valid and comparable health, health-related and well-being data over a range of key domains for adult and older adult populations in nationally representative samples To examine patterns and dynamics of age-related changes in health and well-being using longitudinal follow-up of a cohort as they age, and to investigate socio-economic consequences of these health changes To supplement and cross-validate self-reported measures of health and the anchoring vignette approach to improving comparability of self-reported measures, through measured performance tests for selected health domains To collect health examination and biomarker data that improves reliability of morbidity and risk factor data and to objectively monitor the effect of interventions

Additional Objectives: To generate large cohorts of older adult populations and comparison cohorts of younger populations for following-up intermediate outcomes, monitoring trends, examining transitions and life events, and addressing relationships between determinants and health, well-being and health-related outcomes To develop a mechanism to link survey data to demographic surveillance site data To build linkages with other national and multi-country ageing studies To improve the methodologies to enhance the reliability and validity of health outcomes and determinants data To provide a public-access information base to engage all stakeholders, including national policy makers and health systems planners, in planning and decision-making processes about the health and well-being of older adults

Methods: SAGE's first full round of data collection included both follow-up and new respondents in most participating countries. The goal of the sampling design was to obtain a nationally representative cohort of persons aged 50 years and older, with a smaller cohort of persons aged 18 to 49 for comparison purposes. In the older households, all persons aged 50+ years (for example, spouses and siblings) were invited to participate. Proxy respondents were identified for respondents who were unable to respond for themselves. Standardized SAGE survey instruments were used in all countries consisting of five main parts: 1) household questionnaire; 2) individual questionnaire; 3) proxy questionnaire; 4) verbal autopsy questionnaire; and, 5) appendices including showcards. A VAQ was completed for deaths in the household over the last 24 months. The procedures for including country-specific adaptations to the standardized questionnaire and translations into local languages from English follow those developed by and used for the World Health Survey.

Content: - Household questionnaire 0000 Coversheet 0100 Sampling Information 0200 Geocoding and GPS Information 0300 Recontact Information 0350 Contact Record 0400 Household Roster 0450 Kish Tables and Household Consent 0500 Housing 0600 Household and Family Support Networks and Transfers 0700 Assets and Household Income 0800 Household Expenditures 0900 Interviewer Observations

• Verbal Autopsy questionnaire Section 1: Information on the Deceased and Date/Place of Death Section 1A7: Vital Registration and Certification Section 2: Information on the Respondent Section 3A: Medical History Associated with Final Illness Section 3B: General Signs and Symptoms Associated with Final Illness Section 3E: History of Injuries/Accidents Section 3G: Health Service Utilization Section 4: Background Section 5A: Interviewer Observations

• Individual questionnaire 1000 Socio-Demographic Characteristics 1500 Work History and Benefits 2000 Health State Descriptions 2500 Anthropometrics, Performance Tests and Biomarkers 3000 Risk Factors and Preventive Health Behaviours 4000 Chronic Conditions and Health Services Coverage 5000 Health Care Utilisation 6000 Social Networks 7000 Subjective Well-Being and Quality of Life (WHOQoL-8 and Day Reconstruction Method) 8000 Impact of Caregiving 9000 Interviewer Assessment

• Proxy Questionnaire Section1 Respondent Characteristics and IQ CODE Section2 Health State Descriptions Section4 Chronic Conditions and Health Services Coverage Section5 Health Care Utilisation

Geographic coverage

National coverage

Analysis unit

households and individuals

Universe

The household section of the survey covered all households in 31 of the 32 federal states in Mexico. Colima was excluded. Institutionalised populations are excluded. The individual section covered all persons aged 18 years and older residing within individual households. As the focus of SAGE is older adults, a much larger sample of respondents aged 50 years and older was selected with a smaller comparative sample of respondents aged 18-49 years.

Kind of data

Sample survey data [ssd]

Sampling procedure

In Mexico strata were defined by locality (metropolitan, urban, rural). All 211 PSUs selected for wave 1 were included in the wave 2 sample. A sub-sample of 211 PSUs was selected from the 797 WHS PSUs for the wave 1 sample. The Basic Geo-Statistical Areas (AGEB) defined by the National Institute of Statistics (INEGI) constitutes a PSU. PSUs were selected probability proportional to three factors: a) (WHS/SAGE Wave 0 50plus): number of WHS/SAGE Wave 0 50-plus interviewed at the PSU, b) (State Population): population of the state to which the PSU belongs, c) (WHS/SAGE Wave 0 PSU at county): number of PSUs selected from the county to which the PSU belongs for the WHS/SAGE Wave 0 The first and third factors were included to reduce geographic dispersion. Factor two affords states with larger populations a greater chance of selection.

All WHS/SAGE Wave 0 individuals aged 50 years or older in the selected rural or urban PSUs and a random sample 90% of individuals aged 50 years or older in metropolitan PSUs who had been interviewed for the WHS/SAGE Wave 0 were included in the SAGE Wave 1 ''primary'' sample. The remaining 10% of WHS/SAGE Wave 0 individuals aged 50 years or older in metropolitan areas were then allocated as a ''replacement'' sample for individuals who could not be contacted or did not consent to participate in SAGE Wave 1. A systematic sample of 1000 WHS/SAGE Wave 0 individuals aged 18-49 across all selected PSUs was selected as the ''primary'' sample and 500 as a ''replacement'' sample.

This selection process resulted in a sample which had an over-representation of individuals from metropolitan strata; therefore, it was decided to increase the number of individuals aged 50 years or older from rural and urban strata. This was achieved by including individuals who had not been part of WHS/SAGE Wave 0 (which became a ''supplementary'' sample), although the household in which they lived included an individual from WHS/SAGE Wave 0. All individuals aged 50 or over were included from rural and urban ''18-49 households'' (that is, where an individual aged 18-49 was included in WHS/SAGE Wave 0) as part of the ''primary supplementary'' sample. A systematic random sample of individuals aged 50 years or older was then obtained from urban and rural households where an individual had already been selected as part of the 50 years and older or 18-49 samples. These individuals then formed part of the ''primary supplementary'' sample and the remainder (that is, those not systematically selected) were allocated to the ''replacement supplementary'' sample. Thus, all individuals aged 50 years or older who lived in households in urban and rural PSUs obtained for SAGE Wave 1 were selected as either a primary or replacement participant. A final ''replacement'' sample for the 50 and over age group was obtained from a systematic sample of all individuals aged 50 or over from households which included the individuals already selected for either the 50 and over or 18-49. This sampling strategy also provided participants who had not been included in WHS/SAGE Wave 0, but lived in a household where an individual had been part of WHS/SAGE Wave 0 (that is, the ''supplementary'' sample), in addition to follow-up of individuals who had been included in the WHS/SAGE Wave 0 sample.

Strata: Locality = 3 PSU: AGEBs = 211 SSU: Households = 6549 surveyed TSU: Individual = 6342 surveyed

Mode of data collection

Face-to-face [f2f], CAPI

Research instrument

The questionnaires were based on the SAGE Wave 1 Questionnaires with some modification and new additions, except for verbal autopsy. SAGE Wave 2 used the 2012 version of the WHO Verbal Autopsy Questionnare. SAGE Wave 1 used an adapted version of the Sample Vital Registration iwth Verbal Autopsy (SAVVY) questionnaire. A Household questionnaire was administered to all households eligible for the study. A Verbal Autopsy questionnaire was administered to 50 plus households only. In follow-up 50 plus household if the death occured since the last wave of the study and in a new 50 plus household if the death occurred in the

Small, fragmented or isolated populations are at risk of population decline due to fitness costs associated with inbreeding and genetic drift. The King Island scrubtit Acanthornis magna greeniana is a critically endangered subspecies of the nominate Tasmanian scrubtit A. m. magna, with an estimated population of < 100 individuals persisting in three patches of swamp forest. The Tasmanian scrubtit is widespread in wet forests on mainland Tasmania. We sequenced the scrubtit genome using PacBio HiFi and undertook a population genomic study of the King Island and Tasmanian scrubtits using a double-digest restriction site-associated DNA (ddRAD) dataset of 5,239 SNP loci. The genome was 1.48 Gb long, comprising 1,518 contigs with an N50 of 7.715 Mb. King Island scrubtits formed one of four overall genetic clusters, but separated into three distinct subpopulations when analysed independently of the Tasmanian scrubtit. Pairwise FST values were greater among the King Island scrubtit subpopulations than among most Tasmanian scrubtit subpopulations. Genetic diversity was lower and inbreeding coefficients were higher in the King Island scrubtit than all except one of the Tasmanian scrubtit subpopulations. We observed crown baldness in 8/15 King Island scrubtits, but 0/55 Tasmanian scrubtits. Six loci were significantly associated with baldness, including one within the DOCK11 gene which is linked to early feather development. Contemporary gene flow between King Island scrubtit subpopulations is unlikely, with further field monitoring required to quantify the fitness consequences of its small population size, low genetic diversity and high inbreeding. Evidence-based conservation actions can then be implemented before the taxon goes extinct. Methods 2.1 Sample collection To obtain indicative genetic diversity metrics across mainland Tasmania, we sampled between five and eleven scrubtits from seven a-priori subpopulations on mainland Tasmania (including Bruny Island) during the non-breeding season (January – March 2021). Due to small population sizes and licensing restrictions on King Island, we sampled five individuals from each of the three locations during the same non-breeding season (Table 1, Figure 1). We trapped scrubtits using a single 6m mist net and one minute of scrubtit song broadcast using portable speakers (ANU animal ethics permit # A2021/33). We sampled blood (< 20 μl per individual) using the standard brachial venepuncture technique with a 0.7mm needle into 70% ethanol. For two individuals from whom we were unable to safely obtain blood, we collected feathers shed during handling. One male Tasmanian scrubtit was collected under licence (see acknowledgements) for genome sequencing, from which organ tissue samples (heart, spleen, kidney, gonads, brain, liver) were taken (Table S1). For each individual we took standard morphometric measurements and scanned for any unusual physical features such as feather abnormalities or skin lesions that may be indicators of poor health. A single observer (CY) sampled and measured all birds, and the maximum capture time was 35 minutes. No birds showed adverse reactions to sampling and all flew off strongly upon release. The fifteen individuals sampled on King Island was the maximum permissible sample size under licence conditions. 2.2 DNA extraction, sexing and sequencing High molecular weight DNA was extracted from flash frozen heart and kidney using the Nanobind Tissue Big DNA Kit v1.0 11/19 (Circulomics). A Qubit fluorometer (Thermo Fisher Scientific) was used to quantify DNA concentrations with the Qubit dsDNA BR assay kit (Thermo Fisher Scientific). RNA was extracted from heart, spleen, kidney, gonads, brain, and liver stored in RNA later using the RNeasy Plus mini Kit (Qiagen) with RNAse-free DNAse (Qiagen) digestion. RNA quality was assessed via Nanodrop (Thermo Fisher Scientific). We extracted DNA for population genomics from blood and feather samples using the Monarch® Genomic DNA Purification Kit (New England BioLabs, Victoria, Australia). We quantified DNA concentrations using a Qubit 3.0 fluorometer (yield range 10.3 – 209 ng μl-1, Table S1) and standardised the concentration of each sample to 10-30 ng µl-1 DNA for 20 – 25 μl and determined the sex of individuals using a polymerase chain reaction (PCR) protocol adapted from Fridolfsson and Ellegren (1999, Supplementary file S1). We arranged the samples on a single 96 well plate, containing five technical replicates of the samples with the highest DNA concentrations, an additional 21 non-technical replicates including all of the King Island samples, five extra samples from mainland Tasmania and one negative control. Double-digest restriction associated DNA (ddRAD) sequencing following Peterson et al. (2012) was undertaken at the Australian Genome Research Facility, Melbourne on an Illumina NovaSeq 6000 platform using 150bp paired-end reads. Samples were first quantified using Quantifluor and visualised on 1 % agarose e-gel to ensure all samples exceeded the minimum input DNA quantity of 50 ng. Three establishment samples with at least 250 ng DNA that were representative of the distribution of the samples (2 Tasmanian scrubtits, 1 King Island scrubtit) were used to determine the optimal combination of restriction enzymes, which were EcoRI and HpyCH4IV. Further details on the library preparation protocol are provided in Supplementary file S1. 2.3 Genome sequencing and assembly Full methodological details of the genome and transcriptome sequencing and assembly are provided in Supplementary file S2. In summary, high molecular weight DNA was sent for PacBio HiFi library preparation with Pippin Prep and sequencing on one single molecule real-time (SMRT) cell of the PacBio Sequel II (Australian Genome Research Facility, Brisbane, Australia). Total RNA was sequenced as 100 bp paired-end reads using Illumina NovaSeq 6000 with Illumina Stranded mRNA library preparation at the Ramaciotti Centre for Genomics (University of New South Wales, Sydney, Australia). Genome assembly was conducted on Galaxy Australia (The Galaxy Community, 2022) following the genome assembly guide (Price & Farquharson, 2022) using HiFiasm v0.16.1 with default parameters (Cheng et al., 2021; Cheng et al., 2022). Transcriptome assembly was conducted on the University of Sydney High Performance Computer, Artemis. Genome annotation was performed using FGENESH++ v7.2.2 (Softberry; (Solovyev et al., 2006)) on a Pawsey Supercomputing Centre Nimbus cloud machine (256 GB RAM, 64 vCPU, 3 TB storage) using the longest open reading frame predicted from the global transcriptome, non-mammalian settings, and optimised parameters supplied with the Corvus brachyrhynchos (American crow) gene-finding matrix. The mitochondrial genome was assembled using MitoHifi v3 (Uliano-Silva et al., 2023). Benchmarking universal single copy orthologs (BUSCO) was used to assess genome, transcriptome and annotation completeness (Manni et al., 2021). 2.4 Bioinformatics pipeline and SNP filtering Raw sequence data were processed using Stacks v2.62 (Catchen et al., 2013) and aligned to the genome with BWA v0.7.17-r1188 (Li & Durbin, 2009). Full details of the bioinformatics pipeline, which produced a variant call format (VCF) file containing 45,488 variants for SNP filtering in R v4.0.3 (R Core team 2020) are provided in Supplementary file S1. We filtered genotyped variants using the “SNPfiltR” v1.0.0 package (DeRaad, 2022) based on (i) minimum read depth (≥ 5), (ii) genotype quality (≥ 20), (iii) maximum read depth (≤ 137), and (iv) allele balance ratio (0.2 – 0.8). Then, using a custom R script, we filtered SNPs based on (i) the level of missing data (< 5%); (ii) minor allele count (MAC ≥ 3), (iii) observed heterozygosity (< 0.6), and (iv) linkage disequilibrium (correlation < 0.5 among loci within 500,000 bp). To ensure that relationships between individuals could be accurately inferred from the data, we used these SNPs and samples to construct a hierarchical clustering dendrogram based on genetic distance, with visual examination of the dendrogram confirming that all 24 replicates paired closely together on long branches (Figure S1). The percentage difference between called genotypes of technical replicates was also used to confirm that genotyping error rates were low after filtering (mean 99.91% ± 0.005% SE similarity between replicates). We therefore removed one of each replicate pair from all further analyses. We also made a higher-level bootstrapped dendrogram by using genetic distances among sampling localities instead of individuals (Figure S2). We used “tess3r” (Caye et al. 2016, 2018) to perform a genome scan for loci under selection, using the Bejamini-Hochberg algorithm (Benjamini & Hochberg, 1995), with a false discovery rate of 1 in 10,000 to correct for multiple testing. Because this method identified zero candidate loci under selection, we also used the gl.outflank function in “dartR” v2.0.4 to implement the OutFLANK method (Whitlock & Letterhos 2015) to infer the distribution of FST for loci unlikely to be strongly affected by spatially diversifying selection. This method also identified zero putatively adaptive loci, leaving a final dataset for formal population genetic analysis containing all 70 originally sampled individuals, 5,239 biallelic SNPs, and an overall missing data level of 0.98 %. The number of SNPs and samples removed from the dataset at each filtering step is provided in Table S2. See accompanying Supplementary File for further information on library preparation, molecular sexing, library preparation, bioinformatics, genome sequencing, assembly and annotation. References cited above are provided in the main document.