COVID-19 Stats & Trends

Context

This dataset seeks to provide insights into what has changed due to policies aimed at combating COVID-19 and evaluate the changes in community activities and its relation to reduced confirmed cases of COVID-19. The reports chart movement trends, compared to an expected baseline, over time (from 2020/02/15 to 2020/02/05) by geography (across 133 countries), as well as some other stats about the country that might help explain the evolution of the disease.

Content

1. Grocery & Pharmacy: Mobility trends for places like grocery markets, food warehouses, farmers' markets, specialty food shops, drug stores, and pharmacies.
2. Parks: Mobility trends for places like national parks, public beaches, marinas, dog parks, plazas, and public gardens.
3. Residential: Mobility trends for places of residence.
4. Retail & Recreation: Mobility trends for places like restaurants, cafes, shopping centers, theme parks, museums, libraries, and movie theaters.
5. Transit stations: Mobility trends for places like public transport hubs such as subway, bus, and train stations.
6. Workplaces: Mobility trends for places of work.
7. Total Cases: Total number of people infected with the SARS-CoV-2.
8. Fatalities: Total number of deaths caused by CoV-19.
9. Government Response Stringency Index: Additive score of nine indicators of government response to CoV-19: School closures, workplace closures, cancellation of public events, public information campaigns, stay at home policies, restrictions on internal movement, international travel controls, testing policy, and contact tracing.
10. COVID-19 Testing: Total number of tests performed.
11. Total Vaccinations: Total number of shots given.
12. Total People Vaccinated: Total number of people given a shot.
13. Total People Fully Vaccinated: Total number of people fully vaccinated (might require two shots of some vaccines).
14. Population: Total number of inhabitants.
15. Population Density per km2: Number of human inhabitants per square kilometer.
16. Health System Index: Overall performance of the health system.
17. Human Development Index (HDI): Summary index based on life expectancy at birth, expected years of schooling for children and mean years of schooling for adults, and GNI per capita.
18. GDP (PPP) per capita: Gross Domestic Product (GDP) per capita based on Purchasing Power Parity (PPP), taking into account the relative cost of local goods, services and inflation rates of the country, rather than using international market exchange rates, which may distort the real differences in per capita income.
19. Elderly Population (percentage): Percentage of the population above the age of 65 years old.

References & Acknowledgements

Bing COVID-19 data. Available at: https://github.com/microsoft/Bing-COVID-19-Data COVID-19 Community Mobility Report. Available at: https://www.google.com/covid19/mobility/ COVID-19: Government Response Stringency Index. Available at: https://ourworldindata.org/grapher/covid-stringency-index Coronavirus (COVID-19) Testing. Available at: https://github.com/owid/covid-19-data/blob/master/public/data/testing/covid-testing-all-observations.csv Coronavirus (COVID-19) Vaccination. Available at: https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/vaccinations/vaccinations.csv List of countries and dependencies by population. Available at: https://www.kaggle.com/tanuprabhu/population-by-country-2020 List of countries and dependencies by population density. Available at: https://www.kaggle.com/tanuprabhu/population-by-country-2020 List of countries by Human Development Index. Available at: http://hdr.undp.org/en/data Measuring Overall Health System Performance. Available at: https://www.who.int/healthinfo/paper30.pdf?ua=1 List of countries by GDP (PPP) per capita. Available at: https://data.worldbank.org/indicator/NY.GDP.PCAP.PP.CD List of countries by age structure (65+). Available at: https://data.worldbank.org/indicator/SP.POP.65UP.TO.ZS

Authors

• Diogo Silva, up201706892@fe.up.pt

1. The hypothesis that maternal effects act as an adaptive bridge in translating maternal environments into offspring phenotypes and thereby affecting population dynamics has not been studied in the well-controlled fields. 2. In this study, the effects of maternal population-density on offspring stress axis, reproduction and population dynamics were studied in root voles (Microtus oeconomus). Parental enclosures for breeding offspring were established by introducing 6 adults per sex into each of 4 (low density) and 30 adults per sex into each of another 4 (high density) enclosures. Live-trapping started 2 weeks after. Offspring captured at age of 10-20 days were removed to laboratory, housed under laboratory conditions until puberty, and subsequently used to establish offspring populations in these same enclosures, after parental populations had been removed. Offspring from each of the 2 parental sources were assigned into 4 enclosures with 2 for each of the 2 density treatments used in establishing parental populations (referred to as LL and LH for maternally-unstressed offspring, assigned in low- and high-density, and HL and HH for maternally-stressed offspring, assigned in low- and high-density). Fecal corticosterone metabolites (FCM) levels, offspring reproduction traits and population dynamics were tested following repeated live-trapping over 2 seasons. 3. Differential fluctuations in population size were observed between maternally density-stressed and unstressed offspring. Populations in LL and LH groups changed significantly in responding to initial density, and reached the similar levels at beginning of the second trapping season. Populations in HL and HH groups, however, were remained relatively steady, and in HL group the low population size was sustained until end of experiment. Maternal density-stress was associated with FCM elevations, reproduction suppression, and body mass decrease at sexual maturity in offspring. The FCM elevations and reproduction suppression were independent of offspring population density and correlated with decreased offspring quality. 4. These findings indicate that intrinsic state alterations induced by maternal stress impair offspring capacity in response to immediate environment, and these alterations are likely mediated by maternal stress system. The maladaptive reproduction suppression seen in HL group suggests intrinsic population density as one of ecological factors generating delayed density-dependent effects.

From 2015 to 2022, Northern and Western Europe had the highest density of "right to be forgotten" or "right to erasure" requests issued to Google and Bing, with 37 and 26 appeals per 10 thousand inhabitants in the respective regions.

As of March 2025, Google represented 79.1 percent of the global online search engine market on desktop devices. Despite being much ahead of its competitors, this represents the lowest share ever recorded by the search engine in these devices for over two decades. Meanwhile, its long-time competitor Bing accounted for 12.21 percent, as tools like Yahoo and Yandex held shares of over 2.9 percent each. Google and the global search market Ever since the introduction of Google Search in 1997, the company has dominated the search engine market, while the shares of all other tools has been rather lopsided. The majority of Google revenues are generated through advertising. Its parent corporation, Alphabet, was one of the biggest internet companies worldwide as of 2024, with a market capitalization of 2.02 trillion U.S. dollars. The company has also expanded its services to mail, productivity tools, enterprise products, mobile devices, and other ventures. As a result, Google earned one of the highest tech company revenues in 2024 with roughly 348.16 billion U.S. dollars. Search engine usage in different countries Google is the most frequently used search engine worldwide. But in some countries, its alternatives are leading or competing with it to some extent. As of the last quarter of 2023, more than 63 percent of internet users in Russia used Yandex, whereas Google users represented little over 33 percent. Meanwhile, Baidu was the most used search engine in China, despite a strong decrease in the percentage of internet users in the country accessing it. In other countries, like Japan and Mexico, people tend to use Yahoo along with Google. By the end of 2024, nearly half of the respondents in Japan said that they had used Yahoo in the past four weeks. In the same year, over 21 percent of users in Mexico said they used Yahoo.

The Rural Access Index (RAI) is a measure of access, developed by the World Bank in 2006. It was adopted as Sustainable Development Goal (SDG) indicator 9.1.1 in 2015, to measure the accessibility of rural populations. It is currently the only indicator for the SDGs that directly measures rural access.The RAI measures the proportion of the rural population that lives within 2 km of an all-season road. An all-season road is one that is motorable all year, but may be temporarily unavailable during inclement weather (Roberts, Shyam, & Rastogi, 2006). This dataset implements and expands on the most recent official methodology put forward by the World Bank, ReCAP's 2019 RAI Supplemental Guidelines. This is, to date, the only publicly available application of this method at a global scale.MethodologyReCAP's methodology provided new insight on what makes a road all-season and how this data should be handled: instead of removing unpaved roads from the network, the ones that are classified as unpaved are to be intersected with topographic and climatic conditions and, whenever there’s an overlap with excess precipitation and slope, a multiplying factor ranging from 0% to 100% is applied to the population that would access to that road. This present dataset developed by SDSN's SDG Transformation Centre proposes that authorities ability to maintain and remediate road conditions also be taken into account.Data sourcesThe indicator relies on four major items of geospatial data: land cover (rural or urban), population distribution, road network extent and the “all-season” status of those roads.Land cover data (urban/rural distinction)Since the indicator measures the acess rural populations, it's necessary to define what is and what isn't rural. This dataset uses the DegUrba Methodology, proposed by the United Nations Expert Group on Statistical Methodology for Delineating Cities and Rural Areas (United Nations Expert Group, 2019). This approach has been developed by the European Commission Global Human Settlement Layer (GHSL-SMOD) project, and is designed to instil some consistency into the definitions based on population density on a 1-km grid, but adjusted for local situations.Population distributionThe source for population distribution data is WorldPop. This uses national census data, projections and other ancillary data from countries to produce aggregated, 100 m2 population data. Road extentTwo widely recognized road datasets are used: the real-time updated crowd-sourced OpenStreetMap (OSM) or the GLOBIO’s 2018 GRIP database, which draws data from official national sources. The reasons for picking the latter are mostly related to its ability to provide information on the surface (pavement) of these roads, to the detriment of the timeliness of the data, which is restrained to the year 2018. Additionally, data from Microsoft Bing's recent Road Detection project is used to ensure completeness. This dataset is completely derived from machine learning methods applied over satellite imagery, and detected 1,165 km of roads missing from OSM.Roads’ all-season statusThe World Bank's original 2006 methodology defines the term all-season as “… a road that is motorable all year round by the prevailing means of rural transport, allowing for occasional interruptions of short duration”. ReCAP's 2019 methodology makes a case for passability equating to the all-season status of a road, along with the assumption that typically the wet season is when roads become impassable, especially so in steep roads that are more exposed to landslides.This dataset follows the ReCAP methodology by creating an passability index. The proposed use of passability factors relies on the following three aspects:• Surface type. Many rural roads in LICs (and even in large high-income countries including the USA and Australia) are unpaved. As mentioned before, unpaved roads deteriorate rapidly and in a different way to paved roads. They are very susceptible to water ingress to the surface, which softens the materials and makes them very vulnerable to the action of traffic. So, when a road surface becomes saturated and is subject to traffic, the deterioration is accelerated. • Climate. Precipitation has a significant effect on the condition of a road, especially on unpaved roads, which predominate in LICs and provide much of the extended connectivity to rural and poor areas. As mentioned above, the rainfall on a road is a significant factor in its deterioration, but the extent depends on the type of rainfall in terms of duration and intensity, and how well the roadside drainage copes with this. While ReCAP suggested the use of general climate zones, we argue that better spatial and temporal resolutions can be acquired through the Copernicus Programme precipitation data, which is made available freely at ~30km pixel size for each month of the year.• Terrain. The gradient and altitude of roads also has an effect on their accessibility. Steep roads become impassable more easily due to the potential for scour during heavy rainfall, and also due to slipperiness as a result of the road surface materials used. Here this is drawn from slope calculated from SRTM Digital Terrain data.• Road maintenance. The ability of local authorities to remediate damaged caused by precipitation and landslides is proposed as a correcting factor to the previous ones. Ideally this would be measured by the % of GDP invested in road construction and maintenance, but this isn't available for all countries. For this reason, GDP per capita is adopted as a proxy instead. The data range is normalized in such a way that a road maxed out in terms of precipitation and slope (accessibility score of 0.25) in a country at the top of the GDP per capita range is brought back at to the higher end of the accessibility score (0.95), while the accessibility score of a road meeting the same passability conditions in a country which GDP per capita is towards the lower end is kept unchanged.Data processingThe roads from the three aforementioned datasets (Bing, GRIP and OSM) are merged together to them is applied a 2km buffer. The populations falling exclusively on unpaved road buffers are multiplied by the resulting passability index, which is defined as the normalized sum of the aforementioned components, ranging from 0.25 to. 0.9, with 0.95 meaning 95% probability that the road is all-season. The index applied to the population data, so, when calculated, the RAI includes the probability that the roads which people are using in each area will be all-season or not. For example, an unpaved road in a flat area with low rainfall would have an accessibility factor of 0.95, as this road is designed to be accessible all year round and the environmental effects on its impassability are minimal.The code for generating this dataset is available on Github at: https://github.com/sdsna/rai

Between 2015 and 2022, Estonia had the highest density of “right to be forgotten” or “right to erasure” requests issued to Google and Microsoft Bing, among other European countries, with almost 59 appeals per 10 thousand inhabitants. Registering the highest number of requests during the analyzed period, France ranked second regarding request density, with 46.2 requests per 10 thousand inhabitants.

AimsThe population pharmacokinetic (PPK) model-based machine learning (ML) approach offers a novel perspective on individual concentration prediction. This study aimed to establish a PPK-based ML model for predicting tacrolimus (TAC) concentrations in Chinese renal transplant recipients.MethodsConventional TAC monitoring data from 127 Chinese renal transplant patients were divided into training (80%) and testing (20%) datasets. A PPK model was developed using the training group data. ML models were then established based on individual pharmacokinetic data derived from the PPK basic model. The prediction performances of the PPK-based ML model and Bayesian forecasting approach were compared using data from the test group.ResultsThe final PPK model, incorporating hematocrit and CYP3A5 genotypes as covariates, was successfully established. Individual predictions of TAC using the PPK basic model, postoperative date, CYP3A5 genotype, and hematocrit showed improved rankings in ML model construction. XGBoost, based on the TAC PPK, exhibited the best prediction performance.ConclusionThe PPK-based machine learning approach emerges as a superior option for predicting TAC concentrations in Chinese renal transplant recipients.

As of September 2023, Google led the search engine market in Singapore with a **** percent share of the market. Bing and Yahoo! followed with minor market shares in the same year.

Robustness check: Regression comparing positive sentiment between those who did and did not report assault experience before assault question asked (Bing lexicon).

As of June 2025, Google was the most popular search engine in Italy on desktop devices, with an 83.87 percent share of the search engine market. Considering the same period, Bing only reached 10.4 percent of the market share, while Yahoo! reached 3.43 percent. Furthermore, Italy was among the countries with some of highest desktop market shares for Google in the analyzed month. Internet in Italy: a growing habit... The internet usage in Italy has progressively grown over the last years, with the share of individuals accessing the web in Italy facing a permanent growth, passing from 34.1 percent in 2006, to 80.3 percent in 2023. The daily access to the internet in the country has also increased, from 40.3 percent in 2015, to 67.6 percent in 2023, although 18.4 percent of Italians claimed to not use the internet at all. ...with regional differences Even though the Internet usage in Italy is constantly growing in every region, the share of Internet users remains unbalanced throughout its territory. Northern Italy has a slightly higher share of Internet users in than in the other regions, with approximately 82.8 percent of the population in 2023 accessing the web. Meanwhile the southern region had the lowest share of internet users, with 75.7 percent in the same year.

Distribution of Hg N in Eurasian populations.

Linking ecology with evolutionary biology is important to understand how environments drive population and species divergence. Phenotypically diverse Salix species, such as lowland riparian willow trees and middle- to high-elevation multistemmed shrubs and alpine dwarf shrubs, provide opportunities for studying genetic divergence driven by ecological factors. We used amplified fragment length polymorphism (AFLP) to quantify the genetic variation of 185 individuals from nine populations of four Salix species in Taiwan. Our phylogenetic analyses distinguished two riparian species and the separation of riparian species from multistemmed and dwarf shrub species. Variance partitioning for the total data found that environment explained a substantially larger proportion of genetic variation than geography. However, no genetic variation was explained by geography alone when only compared within and between species. Spatially structured regional environmental effects explained more variation than pure environments in most comparisons within and between species, suggesting that unmeasured environmental variables and/or past demographic histories played important roles in shaping population and species divergence. Based on forward selection analysis, annual mean temperature, aspect, and fraction of absorbed photosynthetically active radiation were the most influential ecological factors in shaping genetic variation within and between species. Nevertheless, different combinations of environmental variables correlated significantly with genetic variation within and between species. We identified eight AFLP loci that potentially evolved under selection intraspecifically using different outlier detection methods. These loci correlated with more than one environmental variable, suggesting local adaptation along environmental gradients at the population level.

Genetic diversity of Dendrolimus punctatusLocations of populations of Dendrolimus punctatus sampled, including the sample sizes (N), frequencies of COI and Ribotypes per population (Nh), and estimates of haplotype diversity (h) and nucleotide diversity (π) for each populations.Table S1-4.docx

ps-places-metadata-v1.01

SUMMARY

This dataset comprises a pair of layers, (points and polys) which attempt to better locate "populated places" in NZ. Populated places are defined here as settled areas, either urban or rural where densitys of around 20 persons per hectare exist, and something is able to be seen from the air.

RATIONALE

The only liberally licensed placename dataset is currently LINZ geographic placenames, which has the following drawbacks: - coordinates are not place centers but left most label on 260 series map - the attributes are outdated

METHODOLOGY

This dataset necessarily involves cleaving the linz placenames set into two, those places that are poplulated, and those unpopulated. Work was carried out in four steps. First placenames were shortlisted according to the following criterion: - all places that rated at least POPL in the linz geographic places layer, ie POPL, METR or TOWN or USAT were adopted. - Then many additional points were added from a statnz meshblock density analysis.
- Finally remaining points were added from a check against linz residential polys, and zenbu poi clusters.

Spelling is broadly as per linz placenames, but there are differences for no particular reason. Instances of LINZ all upper case have been converted to sentance case. Some places not presently in the linz dataset are included in this set, usually new places, or those otherwise unnamed. They appear with no linz id, and are not authoritative, in some cases just wild guesses.

Density was derived from the 06 meshblock boundarys (level 2, geometry fixed), multipart conversion, merging in 06 usually resident MB population then using the formula pop/area*10000. An initial urban/rural threshold level of 0.6 persons per hectare was used.

Step two was to trace the approx extent of each populated place. The main purpose of this step was to determine the relative area of each place, and to create an intersection with meshblocks for population. Step 3 involved determining the political center of each place, broadly defined as the commercial center.

Tracing was carried out at 1:9000 for small places, and 1:18000 for large places using either bing or google satellite views. No attempt was made to relate to actual town 'boundarys'. For example large parks or raceways on the urban fringe were not generally included. Outlying industrial areas were included somewhat erratically depending on their connection to urban areas.

Step 3 involved determining the centers of each place. Points were overlaid over the following layers by way of a base reference:

a. original linz placenames b. OSM nz-locations points layer c. zenbu pois, latest set as of 5/4/11 d. zenbu AllSuburbsRegions dataset (a heavily hand modified) LINZ BDE extract derived dataset courtesy Zenbu. e. LINZ road-centerlines, sealed and highway f. LINZ residential areas, g. LINZ building-locations and building footprints h. Olivier and Co nz-urban-north and south

Therefore in practice, sources c and e, form the effective basis of the point coordinates in this dataset. Be aware that e, f and g are referenced to the LINZ topo data, while c and d are likely referenced to whatever roading dataset google possesses. As such minor discrepencys may occur when moving from one to the other.

Regardless of the above, this place centers dataset was created using the following criteria, in order of priority:

• attempts to represent the present (2011) subjective 'center' of each place as defined by its commercial/retail center ie. mainstreets where they exist, any kind of central retail cluster, even a single shop in very small places.
• the coordinate is almost always at the junction of two or more roads.
• most of the time the coordinate is at or near the centroid of the poi cluster
• failing any significant retail presence, the coordinate tends to be placed near the main road junction to the community.
• when the above criteria fail to yield a definitive answer, the final criteria involves the centroids of: . the urban polygons . the clusters of building footprints/locations.

To be clear the coordinates are manually produced by eye without any kind of computation. As such the points are placed approximately perhaps plus or minus 10m, but given that the roads layers are not that flash, no attempt was made to actually snap the coordinates to the road junctions themselves.

The final step involved merging in population from SNZ meshblocks (merge+sum by location) of popl polys). Be aware that due to the inconsistent way that meshblocks are defined this will result in inaccurate populations, particular small places will collect population from their surrounding area. In any case the population will generally always overestimate by including meshblocks that just nicked the place poly. Also there are a couple of dozen cases of overlapping meshblocks between two place polys and these will double count. Which i have so far made no attempt to fix.

Merged in also tla and regions from SNZ shapes, a few of the original linz atrributes, and lastly grading the size of urban areas according to SNZ 'urban areas" criteria. Ie: class codes:

1. Not used.
2. main urban area 30K+
3. secondary urban area 10k-30K
4. minor urban area 1k-10k
5. rural center 300-1K
6. village -300

Note that while this terminology is shared with SNZ the actual places differ owing to different decisions being made about where one area ends an another starts, and what constiutes a suburb or satellite. I expect some discussion around this issue. For example i have included tinwald and washdyke as part of ashburton and timaru, but not richmond or waikawa as part of nelson and picton. Im open to discussion on these.

No attempt has or will likely ever be made to locate the entire LOC and SBRB data subsets. We will just have to wait for NZFS to release what is thought to be an authoritative set.

PROJECTION

Shapefiles are all nztm. Orig data from SNZ and LINZ was all sourced in nztm, via koordinates, or SNZ. Satellite tracings were in spherical mercator/wgs84 and converted to nztm by Qgis. Zenbu POIS were also similarly converted.

ATTRIBUTES

Shapefile: Points id : integer unique to dataset name : name of popl place, string class : urban area size as above. integer tcode : SNZ tla code, integer rcode : SNZ region code, 1-16, integer area : area of poly place features, integer in square meters. pop : 2006 usually resident popluation, being the sum of meshblocks that intersect the place poly features. Integer lid : linz geog places id desc_code : linz geog places place type code

Shapefile: Polygons gid : integer unique to dataset, shared by points and polys name : name of popl place, string, where spelling conflicts occur points wins area : place poly area, m2 Integer

LICENSE

Clarification about the minorly derived nature of LINZ and google data needs to be sought. But pending these copyright complications, the actual points data is essentially an original work, released as public domain. I retain no copyright, nor any responsibility for data accuracy, either as is, or regardless of any changes that are subsequently made to it.

Peter Scott 16/6/2011

v1.01 minor spelling and grammar edits 17/6/11

Tianshan Mountain provides a model for studying biological evolution and speciation. Here we assess the evolutionary history of the Ostrinia furnacalis and Ostrinia nubilalis, which are sympatric in the Yili River Valley in Xinjiang, China.

Our study is based on the historical gene flow analyses of two species by using three mitochondrial DNA (mtDNA, COI & COII & Cytb) and four nuclear DNA (nuDNA, EF-1α & Wingless & RPS5 & CAD) markers obtained from representatives of HC (Huocheng), YN (Yining), XY (Xinyuan) and MNS (Manasi).

Our results reveal that there is a strong asymmetrical gene flow pattern between the four populations. The population migratory pathways between these different populations show inflow into HC and YN, outflow from XY, and that MNS maintained a flow balance. Bayesian divergence time dating based on the COI gene suggest the genetic divergence between the two species in this area may have occurred in the late-Pleistocene (0.003–0.0127 Mya). Neutrality tests (Tajima's D, Fu's Fs) and mismatch distribution test results suggest that population expansion events may not have occurred in the recent past, which may follow the 'mountain isolation' hypothesis. The ML and BI trees of the mtDNA haplotype dataset show that ECB haplotypes are clustered together in a distinct clade and are clearly separate from ACB haplotypes. However, the geographical pattern of haplotype distribution is less clear and there is no strong correspondence between haplotypes and their geographical pattern for both ACB and ECB, implying that there has been frequent gene flow among the geographic populations in the Tianshan Mountains.

These findings confirm that geological factors play an important role in driving genetic patterns.

BackgroundPatients with esophageal cancer (EC) frequently experience depression following neoadjuvant therapy and surgery, a condition that may trigger systemic inflammation, suppress antitumor immunity, and alter immune-inflammatory pathways in the tumor microenvironment (TME), potentially contributing to residual tumor progression and theoretically worsening patient prognosis. This study aimed to investigate the interrelationship between depression and prognosis in patients with EC, with a focus on immune-inflammatory biomarkers.MethodsThis single-center retrospective trial was conducted at the National Cancer Center/Cancer Hospital of the Chinese Academy of Medical Sciences. A total of 319 patients who underwent minimally invasive esophagectomy between November 2023 and December 2024 were enrolled. Least absolute shrinkage and selection operator (LASSO) regression in combination with multivariate Cox and logistic regression were employed to identify the main impact indicators of relapse-free survival (RFS) and depression. The developed predictive model was evaluated using calibration plots, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA). Internal validation was carried out using a 7:3 data split.ResultsLASSO and Cox regression identified clinical stage (hazard ratio [HR]=2.472, P=0.003), the preoperative systemic inflammatory index (SII, HR=1.001, P

Estimated ages of Hg N and its sub-haplogroups.

Y-STRs diversity of Hg N sub-haplogroups.

aStatistic results corrected by Greenhouse–Geisser, as P value

Note: samples were merged by language families.