Number and percentage of live births, by month of birth, 1991 to most recent year.

This dataset provides a global gridded (5 arc-min resolution) detailed annual net-migration dataset for 2000-2019. We also provide global annual birth and death rate datasets – that were used to estimate the net-migration – for same years. The dataset is presented in details, with some further analyses, in the following publication. Please cite this paper when using data.

Niva et al. 2023. World's human migration patterns in 2000-2019 unveiled by high-resolution data. Nature Human Behaviour 7: 2023–2037. Doi: https://doi.org/10.1038/s41562-023-01689-4

You can explore the data in our online net-migration explorer: https://wdrg.aalto.fi/global-net-migration-explorer/

Short introduction to the data

For the dataset, we collected, gap-filled, and harmonised:

a comprehensive national level birth and death rate datasets for altogether 216 countries or sovereign states; and

sub-national data for births (data covering 163 countries, divided altogether into 2555 admin units) and deaths (123 countries, 2067 admin units).

These birth and death rates were downscaled with selected socio-economic indicators to 5 arc-min grid for each year 2000-2019. These allowed us to calculate the 'natural' population change and when this was compared with the reported changes in population, we were able to estimate the annual net-migration. See more about the methods and calculations at Niva et al (2023).

We recommend using the data either over multiple years (we provide 3, 5 and 20 year net-migration sums at gridded level) or then aggregated over larger area (we provide adm0, adm1 and adm2 level geospatial polygon files). This is due to some noise in the gridded annual data.

Due to copy-right issues we are not able to release all the original data collected, but those can be requested from the authors.

List of datasets

Birth and death rates:

raster_birth_rate_2000_2019.tif: Gridded birth rate for 2000-2019 (5 arc-min; multiband tif)

raster_death_rate_2000_2019.tif: Gridded death rate for 2000-2019 (5 arc-min; multiband tif)

tabulated_adm1adm0_birth_rate.csv: Tabulated sub-national birth rate for 2000-2019 at the division to which data was collected (subnational data when available, otherwise national)

tabulated_ adm1adm0_death_rate.csv: Tabulated sub-national death rate for 2000-2019 at the division to which data was collected (subnational data when available, otherwise national)

Net-migration:

raster_netMgr_2000_2019_annual.tif: Gridded annual net-migration 2000-2019 (5 arc-min; multiband tif)

raster_netMgr_2000_2019_3yrSum.tif: Gridded 3-yr sum net-migration 2000-2019 (5 arc-min; multiband tif)

raster_netMgr_2000_2019_5yrSum.tif: Gridded 5-yr sum net-migration 2000-2019 (5 arc-min; multiband tif)

raster_netMgr_2000_2019_20yrSum.tif: Gridded 20-yr sum net-migration 2000-2019 (5 arc-min)

polyg_adm0_dataNetMgr.gpkg: National (adm 0 level) net-migration geospatial file (gpkg)

polyg_adm1_dataNetMgr.gpkg: Provincial (adm 1 level) net-migration geospatial file (gpkg) (if not adm 1 level division, adm 0 used)

polyg_adm2_dataNetMgr.gpkg: Communal (adm 2 level) net-migration geospatial file (gpkg) (if not adm 2 level division, adm 1 used; and if not adm 1 level division either, adm 0 used)

Files to run online net migration explorer

masterData.rds and admGeoms.rds are related to our online ‘Net-migration explorer’ tool (https://wdrg.aalto.fi/global-net-migration-explorer/). The source code of this application is available in https://github.com/vvirkki/net-migration-explorer. Running the application locally requires these two .rds files from this repository.

Metadata

Grids:

Resolution: 5 arc-min (0.083333333 degrees)

Spatial extent: Lon: -180, 180; -90, 90 (xmin, xmax, ymin, ymax)

Coordinate ref system: EPSG:4326 - WGS 84

Format: Multiband geotiff; each band for each year over 2000-2019

Units:

Birth and death rates: births/deaths per 1000 people per year

Net-migration: persons per 1000 people per time period (year, 3yr, 5yr, 20yr, depending on the dataset)

Geospatial polygon (gpkg) files:

Spatial extent: -180, 180; -90, 83.67 (xmin, xmax, ymin, ymax)

Temporal extent: annual over 2000-2019

Coordinate ref system: EPSG:4326 - WGS 84

Format: gkpk

Units:

Net-migration: persons per 1000 people per year

Permanent group fissions are thought to represent the tipping point at which a group has become too large and therefore splits into two, allowing for an evaluation of the consequences of living in too large a group and if fission can alleviate those costs. We first examined how adult female activity budgets (feeding, moving, resting) differed among periods surrounding (i.e., before and after) multiple fission events, accounting for seasonal variation, and using five mixed-effects beta regression models. We then assessed how rates of agonism differed among periods surrounding these fission events using two negative binomial models, one examining all agonistic interactions and one focusing on agonistic interactions that were lost. Our third analysis used a generalized linear mixed model to investigate a female’s likelihood of conception in a given month, based on her individual characteristics, which post-fission group size she joined, and whether that month fell before vs. after fission, vs. neither. Finally, we used a mixed effects Cox proportional hazards model to evaluate the relationship between infant survival, whether the infant’s mother joined the small vs. large post-fission group, and whether the month in which the infant was born fell before vs. after fission vs. neither. Here we present the three datasets used for these analyses, thus presenting individualized records of both behavioral and life history variables in relation to group fissions. Methods The datasets relate to seven fission events that occurred between 1999 and 2019 in the blue monkey population inhabiting the Kakamega Forest, western Kenya. We used data from all seven fissions for records of female conceptions and infant survival and data from the last five fissions only (2008 to 2019) for records of female behavior, because only these last five fissions occurred while the long-term monitoring protocol included focal animal follows of adult females, which allowed systematic recording of activity. Throughout the study period, a team of trained observers monitored the study groups for all or part of a day on a near daily basis. All group members could be identified as individuals. Observers documented which individuals were present and whether any sub-grouping occurred, meaning that group members were separated into two parties that traveled and foraged separately for at least part of the day. They also recorded all observed agonistic interactions, noting winners and losers when one and only one animal (the loser) showed submission. Beginning in September 2006, the team also conducted systematic 30-minute focal animal follows of adult females, selecting subjects to maintain even sampling across females and across the morning (until 10:30 AM), midday (10:30 AM-14:30 PM) and afternoon (14:30 and later). During focal follows, observers recorded the subject’s activity at 1-minute intervals: main activity categories included feeding (if the subject ingested food on or within 2 sec of the minute mark), moving (involving hindlimb locomotion), and resting. Observers also noted the food item if the focal subject was feeding and the identity of any social partner. Observers recorded all occurrences of agonistic interactions involving the focal subject during focal follows; agonistic interactions between the same opponents were considered separate events if there was a lull in aggressive behavior for at least 30 seconds. We used the census data to identify periods of sub-grouping. Specifically, we identified a sub-grouping period as when the group was split into spatially distinct parties on at least five days, and consecutive sub-grouping days were less than 14 days apart. We considered a fission to be complete when the two sub-groups had their first aggressive intergroup encounter. We designated four 60-day periods representing different times relative to each sub-grouping period. The earliest period was centered on the day that fell a year before the onset of sub-grouping. The last day of the second period fell immediately (a week) before the onset of sub-grouping, and the first day of the third period fell immediately (a week) after fission was complete. The fourth and latest period was centered on the day that fell one year after the date of fission. We aggregated activity records from focal follows for each female in each of the four periods. We calculated individuals’ activity budgets for each period by dividing the total number of instantaneous records when a female performed a given activity by the total number of instantaneous records when she was a focal subject. We accounted for seasonal variation by calculating a population-wide mean percentage for a given activity for each month using all focal follows from 2006 to 2013. We then calculated the mean during the time of year matching each 60-day analysis period as a weighted mean based on the number of days of each month that matched the analysis period. Finally, we expressed the percentage of a female’s activity budget as a deviation in percentage points from the mean time spent on that activity during the same time of year. To investigate how agonism rates varied by period, we aggregated all agonism that a female experienced during her focal samples in each period, breaking it down into total agonism and agonism losses. Agonistic interactions included aggressive (spatial displacements, threats, chases, contact aggression) and submissive (flee, cower, gecker, trill) behavior. Females did not need to be present in all four periods to be included in either analysis. However, we excluded females that were sampled for less than 6 hours in a given period, as these females were prone to having outlying data values. To analyze likelihood of conception, we focused on females who were adults at any time from October 1997 to December 2022. Females that were already reproductively mature (i.e., had already conceived their first offspring) in October 1997 were included in the dataset beginning that month. Females that matured after October 1997 were added to the dataset starting the month after their first confirmed conception. For females that died during the study period, the last month we included in the dataset was 7 months before their death or the month of their last birth, whichever occurred later. All other females remained in the data set through December 2022. We excluded the month of a female’s first conception because it had missing values for certain predictors, including time since last conception. Conceptions could be confirmed only if an offspring was born, whether it was first seen alive or dead (either stillbirth or peri-natal death). Therefore, the month of a female’s first conception fell 176 days before her first birth of a full-term infant (whether living or stillborn). For one female that had a miscarriage after her first confirmed birth, we omitted all months from seven months before the miscarriage to the month after the subsequent conception (because we could not confirm a value for the time since last conception for these months). We assigned each adult female a monthly reproductive status (pregnant, gave birth, conceived, or non-reproductive). We categorized a female as “pregnant” if she was pregnant the entire month, “gave birth” if she gave birth during that month, “conceived” if she conceived during that month, and “non-reproductive” if no other status applied. We created three categorical variables to assess the influence of fission on probability of conception at six months, one year, and two years. We calculated time since last conception and maternal age to the nearest month. We classified lactation stage as one of five categories based on the age of her most recent surviving infant: 1 (infant age < 5 months), 2 (infant age 5-9 months), 3 (infant age 10-15 months), 4 (infant age 15-32 months), and 5 (infant age > 32 months). We also created an exposure variable that equaled the number of days in each month in which a female could conceive. For months during which females gave birth, this value was the number of days remaining in the month after the birth. Pregnant females, who took a value of 0, were excluded from the model of conception probability. We added a variable identifying which post-fission group a female ended up in for months falling within 2 years before or after a fission event. For the infant survival analysis, we created three categorical variables to assess the influence of fission on infant survival, assigning each infant as being born before vs. after fission vs. neither, and using timescales of six months, one year, and two years to assess “before” and “after”. We used the infant’s mother’s age at the time of the infant’s birth and designated whether the infant was born during the peak birth season (December-March) or not. We added a variable identifying which post-fission group an infant’s mother ended up in for infants born two years before or after fission.

The 'Climate Just' Map Tool shows the geography of England’s vulnerability to climate change at a neighbourhood scale. The Climate Just Map Tool shows which places may be most disadvantaged through climate impacts. It aims to raise awareness about how social vulnerability combined with exposure to hazards, like flooding and heat, may lead to uneven impacts in different neighbourhoods, causing climate disadvantage. Climate Just Map Tool includes maps on: Flooding (river/coastal and surface water) Heat Fuel poverty. The flood and heat analysis for England is based on an assessment of social vulnerability in 2011 carried out by the University of Manchester. This has been combined with national datasets on exposure to flooding, using Environment Agency data, and exposure to heat, using UKCP09 data. Data is available at Middle Super Output Area (MSOA) level across England. Summaries of numbers of MSOAs are shown in the file named Climate Just-LA_summaries_vulnerability_disadvantage_Dec2014.xls Indicators include: Climate Just-Flood disadvantage_2011_Dec2014.xlsx Fluvial flood disadvantage indexPluvial flood disadvantage index (1 in 30 years)Pluvial flood disadvantage index (1 in 100 years)Pluvial flood disadvantage index (1 in 1000 years) Climate Just-Flood_hazard_exposure_2011_Dec2014.xlsx Percentage of area at moderate and significant risk of fluvial floodingPercentage of area at risk of surface water flooding (1 in 30 years)Percentage of area at risk of surface water flooding (1 in 100 years)Percentage of area at risk of surface water flooding (1 in 1000 years) Climate Just-SSVI_indices_2011_Dec2014.xlsx Sensitivity - flood and heatAbility to prepare - floodAbility to respond - floodAbility to recover - floodEnhanced exposure - floodAbility to prepare - heatAbility to respond - heatAbility to recover - heatEnhanced exposure - heatSocio-spatial vulnerability index - floodSocio-spatial vulnerability index - heat Climate Just-SSVI_indicators_2011_Dec2014.xlsx % children < 5 years old% people > 75 years old% people with long term ill-health/disability (activities limited a little or a lot)% households with at least one person with long term ill-health/disability (activities limited a little or a lot)% unemployed% in low income occupations (routine & semi-routine)% long term unemployed / never worked% households with no adults in employment and dependent childrenAverage weekly household net income estimate (equivalised after housing costs) (Pounds)% all pensioner households% households rented from social landlords% households rented from private landlords% born outside UK and IrelandFlood experience (% area associated with past events)Insurance availability (% area with 1 in 75 chance of flooding)% people with % unemployed% in low income occupations (routine & semi-routine)% long term unemployed / never worked% households with no adults in employment and dependent childrenAverage weekly household net income estimate (equivalised after housing costs) (Pounds)% all pensioner households% born outside UK and IrelandFlood experience (% area associated with past events)Insurance availability (% area with 1 in 75 chance of flooding)% single pensioner households% lone parent household with dependent children% people who do not provide unpaid care% disabled (activities limited a lot)% households with no carCrime score (IMD)% area not roadDensity of retail units (count /km2)% change in number of local VAT-based units% people with % not home workers% unemployed% in low income occupations (routine & semi-routine)% long term unemployed / never worked% households with no adults in employment and dependent childrenAverage weekly household net income estimate (Pounds)% all pensioner households% born outside UK and IrelandInsurance availability (% area with 1 in 75 chance of flooding)% single pensioner households% lone parent household with dependent children% people who do not provide unpaid care% disabled (activities limited a lot)% households with no carTravel time to nearest GP by walk/public transport (mins - representative time)% of at risk population (no car) outside of 15 minutes by walk/public transport to nearest GP Number of GPs within 15 minutes by walk/public transport Number of GPs within 15 minutes by car Travel time to nearest hospital by walk/public transport (mins - representative time)Travel time to nearest hospital by car (mins - representative time)% of at risk population outside of 30 minutes by walk/PT to nearest hospitalNumber of hospitals within 30 minutes by walk/public transport Number of hospitals within 30 minutes by car % people with % not home workersChange in median house price 2004-09 (Pounds)% area not green space Area of domestic buildings per area of domestic gardens (m2 per m2)% area not blue spaceDistance to coast (m)Elevation (m)% households with the lowest floor level: Basement or semi-basement% households with the lowest floor level: ground floor% households with the lowest floor level: fifth floor or higher

A single-centre retrospective case control study was conducted. The protocol defined cases as all neonatal deaths or NICU admissions occurring within an eight-year period from 2012 to 2020, although no neonatal deaths occurred during this period following a vaginal breech birth. Controls were identified as the two vaginal breech births directly prior to the case where no neonatal death nor NICU admission occurred. Two previous births were used to prevent bias on the understanding that an adverse outcome can affect clinical decision-making for subsequent births.12 Any NICU admission was included because this indicates a neonate which requires additional observation, tests and/or intervention. Neonates who are not admitted are deemed as generally well.13 Additionally, separation from the baby was considered an important outcome by our Patient and Public Involvement Group,14 who also requested more information on the timing of cord clamping.The study was conducted within the maternity unit at a London District General Hospital which serves a large population of 176,313 people. Two thirds are of white British ethnicity and one third from Black, Asian and Minority Ethnic (BAME) backgrounds. The community the hospital serves is thought of as affluent, with good employment rates, particularly employment in high-end jobs. The hospital itself serves a wider community than the borough it is situated within and has 5000 births per year. It has a level two NICU situated within the maternity unit. The Algorithm was not in use at the site, and none of the authors were employed by the Trust, during the time period covered by the study. Fifteen cases and thirty controls were identified from routine electronic health records. The Medical Record Numbers were sent to the Health Records Department for the complete files to be retrieved. Data were extracted by the lead researcher from the intrapartum care records and recorded anonymously in a Microsoft Excel spreadsheet.A structured data collection tool was developed based on Reitter et al.13 The data collection tool consisted of information usually recorded in the notes during a breech birth and included: lead professional, type of breech, position, epidural, fetal monitoring, meconium, what emerged first, time each part of the breech born, documented manoeuvres used, time performed and information related to the condition of the neonate at birth.To calculate our sample size, based on the work of Reitter et al,11 we hypothesised that the rate of exposure to a pelvis-to-head interval >3 minutes would be 25% among controls and 75% among cases. Using a case:control ratio of 1:2, we determined that 15 independent cases and 30 controls were required to infer an association between a pelvis-to-head interval >3 minutes and the composite neonatal outcome with a confidence interval of 95% and a power of 80%. First, we calculated the time to event interval for variables of interest. We then reported descriptive statistics for all variables, including means, medians and range for continuous variables. Exposures and confounders were converted into binary variables, reflecting the cut-offs used in the Algorithm. These were then tested against the primary outcome using the non-parametric chi-square, or Fisher’s Exact tests where cell frequencies were too small for the chi-square test. Logistic regression analysis was used to test the predictive values of meeting or exceeding the recommended time limits in the Physiological Breech Birth Algorithm. Further logistic regression analyses were conducted with all variables that showed an association with the composite neonatal outcome to determine their predictive value, and additional variables to explore their potential as confounding factors for investigation in future studies. Finally, a Receiver Operating Characteristics (ROC) curve analysis was conducted to compare the sensitivity and specificity of the 7-5-3 minute time limits. All statistical analyses were performed using IBM SPSS version 26.

Context & Content

This dataset features the salaries of 874 nhl players for the 2016/2017 season. I have randomly split the players into a training (612 players) and test (262 players) populations. There are 151 predictor columns (described in column legend section, if you're not familiar with hockey the meaning of some of these may be a bit cryptic!) as well as a leading column with the players 2016/2017 annual salary. For the test population the actual salaries have been broken off into a separate .csv file.

Acknowledgements

Raw excel sheet was acquired http://www.hockeyabstract.com/

Inspiration

Can you build a model to predict NHL player's salaries? What are the best predictors of how much a player will make?

Column Legend

Acronym - Meaning

%FOT - Percentage of all on-ice faceoffs taken by this player.

+/- - Plus/minus

1G - First goals of a game

A/60 - Events Against per 60 minutes, defaults to Corsi, but can be set to another stat

A1 - First assists, primary assists

A2 - Second assists, secondary assists

BLK% - Percentage of all opposing shot attempts blocked by this player

Born - Birth date

C.Close - A player shot attempt (Corsi) differential when the game was close

C.Down - A player shot attempt (Corsi) differential when the team was trailing

C.Tied - A player shot attempt (Corsi) differential when the team was tied

C.Up - A player shot attempt (Corsi) differential when the team was in the lead

CA - Shot attempts allowed (Corsi, SAT) while this player was on the ice

Cap Hit - The player's cap hit

CBar - Crossbars hit

CF - The team's shot attempts (Corsi, SAT) while this player was on the ice

CF.QoC - A weighted average of the Corsi percentage of a player's opponents

CF.QoT - A weighted average of the Corsi percentage of a player's linemates

CHIP - Cap Hit of Injured Player is games lost to injury multiplied by cap hit per game

City - City of birth

Cntry - Country of birth

DAP - Disciplined aggression proxy, which is hits and takeaways divided by minor penalties

DFA - Dangerous Fenwick against, which is on-ice unblocked shot attempts weighted by shot quality

DFF - Dangerous Fenwick for, which is on-ice unblocked shot attempts weighted by shot quality

DFF.QoC - Quality of Competition metric based on Dangerous Fenwick, which is unblocked shot attempts weighted for shot quality

DftRd - Round in which the player was drafted

DftYr - Year drafted

Diff - Events for minus event against, defaults to Corsi, but can be set to another stat

Diff/60 - Events for minus event against, per 60 minutes, defaults to Corsi, but can be set to another stat

DPS - Defensive point shares, a catch-all stats that measures a player's defensive contributions in points in the standings

DSA - Dangerous shots allowed while this player was on the ice, which is rebounds plus rush shots

DSF - The team's dangerous shots while this player was on the ice, which is rebounds plus rush shots

DZF - Shifts this player has ended with an defensive zone faceoff

dzFOL - Faceoffs lost in the defensive zone

dzFOW - Faceoffs win in the defensive zone

dzGAPF - Team goals allowed after faceoffs taken in the defensive zone

dzGFPF - Team goals scored after faceoffs taken in the defensive zone

DZS - Shifts this player has started with an defensive zone faceoff

dzSAPF - Team shot attempts allowed after faceoffs taken in the defensive zone

dzSFPF - Team shot attempts taken after faceoffs taken in the defensive zone

E+/- - A player's expected +/-, based on his team and minutes played

ENG - Empty-net goals

Exp dzNGPF - Expected goal differential after faceoffs taken in the defensive zone, based on the number of them

Exp dzNSPF - Expected shot differential after faceoffs taken in the defensive zone, based on the number of them

Exp ozNGPF - Expected goal differential after faceoffs taken in the offensive zone, based on the number of them

Exp ozNSPF - Expected shot differential after faceoffs taken in the offensive zone, based on the number of them

F.Close - A player unblocked shot attempt (Fenwick) differential when the game was close

F.Down - A player unblocked shot attempt (Fenwick) differential when the team was trailing

F.Tied - A player unblocked shot attempt (Fenwick) differential when the team was tied

F.Up - A player unblocked shot attempt (Fenwick) differential when the team was in the lead. Not the best acronym.

F/60 - Events For per 60 minutes, defaults to Corsi, but can be set to another stat

FA - Unblocked shot attempts allowed (Fenwick, USAT) while this player was on the ice

FF - The team's unblocked shot attempts (Fenwick, USAT) while this player was on the ice

First Name -

FO% - Faceoff winning percentage

FO%vsL - Faceoff winning percentage against lefthanded opponents

FO%vsR - Faceoff winning percentage against righthanded opponents

FOL - The team's faceoff losses...