According to ** percent of Chief Information Security Officers (CISO) from worldwide organizations, an employee or a so-called compromised insider that might inadvertently expose their credentials, giving cybercriminals access to sensitive data, was the most common cause of a data breach. A further ** percent thought a malicious insider, who would intentionally steal the information would most likely cause a data breach in their organization in the next 12 months.

During the third quarter of 2024, data breaches exposed more than *** million records worldwide. Since the first quarter of 2020, the highest number of data records were exposed in the first quarter of ***, more than *** million data sets. Data breaches remain among the biggest concerns of company leaders worldwide. The most common causes of sensitive information loss were operating system vulnerabilities on endpoint devices. Which industries see the most data breaches? Meanwhile, certain conditions make some industry sectors more prone to data breaches than others. According to the latest observations, the public administration experienced the highest number of data breaches between 2021 and 2022. The industry saw *** reported data breach incidents with confirmed data loss. The second were financial institutions, with *** data breach cases, followed by healthcare providers. Data breach cost Data breach incidents have various consequences, the most common impact being financial losses and business disruptions. As of 2023, the average data breach cost across businesses worldwide was **** million U.S. dollars. Meanwhile, a leaked data record cost about *** U.S. dollars. The United States saw the highest average breach cost globally, at **** million U.S. dollars.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. ZIP Code Tabulation Areas (ZCTAs) are approximate area representations of U.S. Postal Service (USPS) ZIP Code service areas that the Census Bureau creates to present statistical data for each decennial census. The Census Bureau delineates ZCTA boundaries for the United States, Puerto Rico, American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands once each decade following the decennial census. Data users should not use ZCTAs to identify the official USPS ZIP Code for mail delivery. The USPS makes periodic changes to ZIP Codes to support more efficient mail delivery. The Census Bureau uses tabulation blocks as the basis for defining each ZCTA. Tabulation blocks are assigned to a ZCTA based on the most frequently occurring ZIP Code for the addresses contained within that block. The most frequently occurring ZIP Code also becomes the five-digit numeric code of the ZCTA. These codes may contain leading zeros. Blocks that do not contain addresses but are surrounded by a single ZCTA (enclaves) are assigned to the surrounding ZCTA. Because the Census Bureau only uses the most frequently occurring ZIP Code to assign blocks, a ZCTA may not exist for every USPS ZIP Code. Some ZIP Codes may not have a matching ZCTA because too few addresses were associated with the specific ZIP Code or the ZIP Code was not the most frequently occurring ZIP Code within any of the blocks where it exists. The ZCTA boundaries in this release are those delineated following the 2010 Census.

Abstract

Moldova's first Demographic and Health Survey (2005 MDHS) is a nationally representative sample survey of 7,440 women age 15-49 and 2,508 men age 15-59 selected from 400 sample points (clusters) throughout Moldova (excluding the Transnistria region). It is designed to provide data to monitor the population and health situation in Moldova; it includes several indicators which follow up on those from the 1997 Moldova Reproductive Health Survey (1997 MRHS) and the 2000 Multiple Indicator Cluster Survey (2000 MICS). The 2005 MDHS used a two-stage sample based on the 2004 Population and Housing Census and was designed to produce separate estimates for key indicators for each of the major regions in Moldova, including the North, Center, and South regions and Chisinau Municipality. Unlike the 1997 MRHS and the 2000 MICS surveys, the 2005 MDHS did not cover the region of Transnistria. Data collection took place over a two-month period, from June 13 to August 18, 2005.

The survey obtained detailed information on fertility levels, abortion levels, marriage, sexual activity, fertility preferences, awareness and use of family planning methods, breastfeeding practices, nutritional status of women and young children, childhood mortality, maternal and child health, adult health, and awareness and behavior regarding HIV infection and other sexually transmitted diseases. Hemoglobin testing was conducted on women and children to detect the presence of anemia. Additional features of the 2005 MDHS include the collection of information on international emigration, language preference for reading printed media, and domestic violence. The 2005 MDHS was carried out by the National Scientific and Applied Center for Preventive Medicine, hereafter called the National Center for Preventive Medicine (NCPM), of the Ministry of Health and Social Protection. ORC Macro provided technical assistance for the MDHS through the USAID-funded MEASURE DHS project. Local costs of the survey were also supported by USAID, with additional funds from the United Nations Children's Fund (UNICEF), the United Nations Population Fund (UNFPA), and in-kind contributions from the NCPM.

MAIN RESULTS

CHARACTERISTICS OF RESPONDENTS

Ethnicity and Religion. Most women and men in Moldova are of Moldovan ethnicity (77 percent and 76 percent, respectively), followed by Ukrainian (8-9 percent of women and men), Russian (6 percent of women and men), and Gagauzan (4-5 percent of women and men). Romanian and Bulgarian ethnicities account for 2 to 3 percent of women and men. The overwhelming majority of Moldovans, about 95 percent, report Orthodox Christianity as their religion.

Residence and Age. The majority of respondents, about 58 percent, live in rural areas. For both sexes, there are proportionally more respondents in age groups 15-19 and 45-49 (and also 45-54 for men), whereas the proportion of respondents in age groups 25-44 is relatively lower. This U-shaped age distribution reflects the aging baby boom cohort following World War II (the youngest of the baby boomers are now in their mid-40s), and their children who are now mostly in their teens and 20s. The smaller proportion of men and women in the middle age groups reflects the smaller cohorts following the baby boom generation and those preceding the generation of baby boomers' children. To some degree, it also reflects the disproportionately higher emigration of the working-age population.

Education. Women and men in Moldova are universally well educated, with virtually 100 percent having at least some secondary or higher education; 79 percent of women and 83 percent of men have only a secondary or secondary special education, and the remainder pursues a higher education. More women (21 percent) than men (16 percent) pursue higher education.

Language Preference. Among women, preferences for language of reading material are about equal for Moldovan (37 percent) and Russian (35 percent) languages. Among men, preference for Russian (39 percent) is higher than for Moldovan (25 percent). A substantial percentage of women and men prefer Moldovan and Russian equally (27 percent of women and 32 percent of men).

Living Conditions. Access to electricity is almost universal for households in Moldova. Ninety percent of the population has access to safe drinking water, with 86 percent in rural areas and 96 percent in urban areas. Seventy-seven percent of households in Moldova have adequate means of sanitary disposal, with 91 percent of households in urban areas and only 67 percent in rural areas.

Children's Living Arrangements. Compared with other countries in the region, Moldova has the highest proportion of children who do not live with their mother and/or father. Only about two-thirds (69 percent) of children under age 15 live with both parents. Fifteen percent live with just their mother although their father is alive, 5 percent live with just their father although their mother is alive, and 7 percent live with neither parent although they are both alive. Compared with living arrangements of children in 2000, the situation appears to have worsened.

FERTILITY

Fertility Levels and Trends. The total fertility rate (TFR) in Moldova is 1.7 births. This means that, on average, a woman in Moldova will give birth to 1.7 children by the end of her reproductive period. Overall, fertility rates have declined since independence in 1991. However, data indicate that fertility rates may have increased in recent years. For example, women of childbearing age have given birth to, on average, 1.4 children at the end of their childbearing years. This is slightly less than the total fertility rate (1.7), with the difference indicating that fertility in the past three years is slightly higher than the accumulation of births over the past 30 years.

Fertility Differentials. The TFR for rural areas (1.8 births) is higher than that for urban areas (1.5 births). Results show that this urban-rural difference in childbearing rates can be attributed almost exclusively to younger age groups.

CONTRACEPTION

Knowledge of Contraception. Knowledge of family planning is nearly universal, with 99 percent of all women age 15-49 knowing at least one modern method of family planning. Among all women, the male condom, IUD, pills, and withdrawal are the most widely known methods of family planning, with over 80 percent of all women saying they have heard of these methods. Female sterilization is known by two-thirds of women, while periodic abstinence (rhythm method) is recognized by almost six in ten women. Just over half of women have heard of the lactational amenorrhea method (LAM), while 40-50 percent of all women have heard of injectables, male sterilization, and foam/jelly. The least widely known methods are emergency contraception, diaphragm, and implants.

Use of Contraception. Sixty-eight percent of currently married women are using a family planning method to delay or stop childbearing. Most are using a modern method (44 percent of married women), while 24 percent use a traditional method of contraception. The IUD is the most widely used of the modern methods, being used by 25 percent of married women. The next most widely used method is withdrawal, used by 20 percent of married women. Male condoms are used by about 7 percent of women, especially younger women. Five percent of married women have been sterilized and 4 percent each are using the pill and periodic abstinence (rhythm method). The results show that Moldovan women are adopting family planning at lower parities (i.e., when they have fewer children) than in the past. Among younger women (age 20-24), almost half (49 percent) used contraception before having any children, compared with only 12 percent of women age 45-49.

MATERNAL HEALTH

Antenatal Care and Delivery Care. Among women with a birth in the five years preceding the survey, almost all reported seeing a health professional at least once for antenatal care during their last pregnancy; nine in ten reported 4 or more antenatal care visits. Seven in ten women had their first antenatal care visit in the first trimester. In addition, virtually all births were delivered by a health professional, in a health facility. Results also show that the vast majority of women have timely checkups after delivering; 89 percent of all women received a medical checkup within two days of the birth, and another 6 percent within six weeks.

CHILD HEALTH

Childhood Mortality. The infant mortality rate for the 5-year period preceding the survey is 13 deaths per 1,000 live births, meaning that about 1 in 76 infants dies before the first birthday. The under-five mortality rate is almost the same with 14 deaths per 1,000 births. The near parity of these rates indicates that most all early childhood deaths take place during the first year of life. Comparison with official estimates of IMRs suggests that this rate has been improving over the past decade.

NUTRITION

Breastfeeding Practices. Breastfeeding is nearly universal in Moldova: 97 percent of children are breastfed. However the duration of breast-feeding is not long, exclusive breastfeeding is not widely practiced, and bottle-feeding is not uncommon. In terms of the duration of breastfeeding, data show that by age 12-15 months, well over half of children (59 percent) are no longer being breastfed. By age 20-23 months, almost all children have been weaned.

Exclusive breastfeeding is not widely practiced and supplementary feeding begins early: 57 percent of breastfed children less than 4 months are exclusively breastfed, and 46 percent under six months are exclusively breastfeed. The remaining breastfed children also consume plain water, water-based liquids or juice, other milk in addition to breast milk, and complimentary foods. Bottle-feeding is fairly widespread in Moldova;

rxivist.org allowed readers to sort and filter the tens of thousands of preprints posted to bioRxiv and medRxiv. Rxivist used a custom web crawler to index all papers posted to those two websites; this is a snapshot of Rxivist the production database. The version number indicates the date on which the snapshot was taken. See the included "README.md" file for instructions on how to use the "rxivist.backup" file to import data into a PostgreSQL database server.

Please note this is a different repository than the one used for the Rxivist manuscript—that is in a separate Zenodo repository. You're welcome (and encouraged!) to use this data in your research, but please cite our paper, now published in eLife.

Previous versions are also available pre-loaded into Docker images, available at blekhmanlab/rxivist_data.

Version notes:

• 2023-03-01
  • The final Rxivist data upload, more than four years after the first and encompassing 223,541 preprints posted to bioRxiv and medRxiv through the end of February 2023.
• 2020-12-07***
  • In addition to bioRxiv preprints, the database now includes all medRxiv preprints as well.
    • The website where a preprint was posted is now recorded in a new field in the "articles" table, called "repo".
  • We've significantly refactored the web crawler to take advantage of developments with the bioRxiv API.
    • The main difference is that preprints flagged as "published" by bioRxiv are no longer recorded on the same schedule that download metrics are updated: The Rxivist database should now record published DOI entries the same day bioRxiv detects them.
  • Twitter metrics have returned, for the most part. Improvements with the Crossref Event Data API mean we can once again tally daily Twitter counts for all bioRxiv DOIs.
    • The "crossref_daily" table remains where these are recorded, and daily numbers are now up to date.
    • Historical daily counts have also been re-crawled to fill in the empty space that started in October 2019.
    • There are still several gaps that are more than a week long due to missing data from Crossref.
    • We have recorded available Crossref Twitter data for all papers with DOI numbers starting with "10.1101," which includes all medRxiv preprints. However, there appears to be almost no Twitter data available for medRxiv preprints.
  • The download metrics for article id 72514 (DOI 10.1101/2020.01.30.927871) were found to be out of date for February 2020 and are now correct. This is notable because article 72514 is the most downloaded preprint of all time; we're still looking into why this wasn't updated after the month ended.
• 2020-11-18
  • Publication checks should be back on schedule.
• 2020-10-26
  • This snapshot fixes most of the data issues found in the previous version. Indexed papers are now up to date, and download metrics are back on schedule. The check for publication status remains behind schedule, however, and the database may not include published DOIs for papers that have been flagged on bioRxiv as "published" over the last two months. Another snapshot will be posted in the next few weeks with updated publication information.
• 2020-09-15
  • A crawler error caused this snapshot to exclude all papers posted after about August 29, with some papers having download metrics that were more out of date than usual. The "last_crawled" field is accurate.
• 2020-09-08
  • This snapshot is misconfigured and will not work without modification; it has been replaced with version 2020-09-15.
• 2019-12-27
  • Several dozen papers did not have dates associated with them; that has been fixed.
  • Some authors have had two entries in the "authors" table for portions of 2019, one profile that was linked to their ORCID and one that was not, occasionally with almost identical "name" strings. This happened after bioRxiv began changing author names to reflect the names in the PDFs, rather than the ones manually entered into their system. These database records are mostly consolidated now, but some may remain.
• 2019-11-29
  • The Crossref Event Data API remains down; Twitter data is unavailable for dates after early October.
• 2019-10-31
  • The Crossref Event Data API is still experiencing problems; the Twitter data for October is incomplete in this snapshot.
  • The README file has been modified to reflect changes in the process for creating your own DB snapshots if using the newly released PostgreSQL 12.
• 2019-10-01
  • The Crossref API is back online, and the "crossref_daily" table should now include up-to-date tweet information for July through September.
  • About 40,000 authors were removed from the author table because the name had been removed from all preprints they had previously been associated with, likely because their name changed slightly on the bioRxiv website ("John Smith" to "J Smith" or "John M Smith"). The "author_emails" table was also modified to remove entries referring to the deleted authors. The web crawler is being updated to clean these orphaned entries more frequently.
• 2019-08-30
  • The Crossref Event Data API, which provides the data used to populate the table of tweet counts, has not been fully functional since early July. While we are optimistic that accurate tweet counts will be available at some point, the sparse values currently in the "crossref_daily" table for July and August should not be considered reliable.
• 2019-07-01
  • A new "institution" field has been added to the "article_authors" table that stores each author's institutional affiliation as listed on that paper. The "authors" table still has each author's most recently observed institution.
    • We began collecting this data in the middle of May, but it has not been applied to older papers yet.
• 2019-05-11
  • The README was updated to correct a link to the Docker repository used for the pre-built images.
• 2019-03-21
  • The license for this dataset has been changed to CC-BY, which allows use for any purpose and requires only attribution.
  • A new table, "publication_dates," has been added and will be continually updated. This table will include an entry for each preprint that has been published externally for which we can determine a date of publication, based on data from Crossref. (This table was previously included in the "paper" schema but was not updated after early December 2018.)
  • Foreign key constraints have been added to almost every table in the database. This should not impact any read behavior, but anyone writing to these tables will encounter constraints on existing fields that refer to other tables. Most frequently, this means the "article" field in a table will need to refer to an ID that actually exists in the "articles" table.
  • The "author_translations" table has been removed. This was used to redirect incoming requests for outdated author profile pages and was likely not of any functional use to others.
  • The "README.md" file has been renamed "1README.md" because Zenodo only displays a preview for the file that appears first in the list alphabetically.
  • The "article_ranks" and "article_ranks_working" tables have been removed as well; they were unused.
• 2019-02-13.1
  • After consultation with bioRxiv, the "fulltext" table will not be included in further snapshots until (and if) concerns about licensing and copyright can be resolved.
  • The "docker-compose.yml" file was added, with corresponding instructions in the README to streamline deployment of a local copy of this database.
• 2019-02-13
  • The redundant "paper" schema has been removed.
  • BioRxiv has begun making the full text of preprints available online. Beginning with this version, a new table ("fulltext") is available that contains the text of preprints that have been processed already. The format in which this information is stored may change in the future; any digression will be noted here.
  • This is the first version that has a corresponding Docker image.

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days (monthly means are available around the 6th of each month). In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 monthly mean data on single levels from 1940 to present".

In many research fields, measurement data containing too many zeros are often called semicontinuous data. For semicontinuous data, the most common method is the two-part model, which establishes the corresponding regression model for both the zero-valued part and the nonzero-valued part. Considering that each part of the two-part regression model often encounters a large number of candidate variables, the variable selection becomes an important problem in semicontinuous data analysis. However, there is little research literature on this topic. To bridge this gap, we propose a new type of variable selection methods for the two-part regression model. In this paper, the Bernoulli-Normal two-part (BNT) regression model is presented, and a variable selection method based on Lasso penalty function is proposed. To solve the problem that Lasso estimator does not have Oracle attribute, we then propose a variable selection method based on adaptive Lasso penalty function. The simulation results show that both methods can select variables for BNT regression model and are easy to implement, and the performance of adaptive Lasso method is superior to the Lasso method. We demonstrate the effectiveness of the proposed tools using dietary intake data to further analyze the important factors affecting dietary intake of patients.

Multi year bottle data 2003-2010
Note: Dataset updated with 2008/EN448 Version 3 data srg/21Mar2013

These data include version 2 data as submitted.
Version 2 NOTES: Version 2 means that Data set has been updated in part of the new data added: (1) 2010 cruises; (2) Previously unavailable Whole cell counts from the previous cruises; (3) Previously unavailable data for Underway stations (mostly date, location).

Data files newly created or updated in this version are indicated with _v2
in file names.
(BCO-DMO Note: for original files as contributed)

Date of creation: 5/12/2011.
Matlab code used for data merging: GM_read_alex_nuts_BTL_v2.m

No data reported for cruises: OC440 (2007) or EN456 (2008).

Data not sampled or lost are indicated with NaN.
Data not available at the time but supposed to arrive are indicated with the \waiting\ flag=-9.99.

Funding:
The cruises from 2003-2004 were supported by NOAA grant NA160P2785 (MERHAB). The cruises from 2005-2010 were jointly funded: NSF grant OCE-0430724, NIEHS grant 1P50-ES01274201 (Woods Hole Center for Oceans and Human Health) and NOAA grant NA06NOS4780245 (GOMTOX). access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson acquisition_description=Hydrographic profiles and water samples were collected with a standard CTD- rosette system with Niskin bottles.\u00a0 Nutrient samples were filtered through Millipore HA filters, placed immediately in a sea water-ice bath for 5\u201310 min, and frozen at \u221218\u00b0C.\u00a0 Concentrations of NO3+NO2, NH4, Si(OH)4 and PO4 were measured with a Bran Luebbe AA3 AutoAnalyzer using standard techniques.

A. fundyense cells were enumerated from water samples using an oligonucleotide probe and methods described in Anderson et al. (2005). Both A. tamarense and A. fundyense occur in the Gulf of Maine, and these are considered to be varieties of the same species. Available molecular probes cannot distinguish between them, and only detailed analysis of the thecal plates on individual cells can provide this resolution\u2014which is not practical for large numbers of field samples.\u00a0 Accordingly, for the purpose of this study, the name A. fundyense is used to refer to both forms.

Anderson, D. M., D. M. Kulis, B. A. Keafer, K. E. Gribble, R. Marin, and C. A. Scholin. 2005. Identification and enumeration of Alexandrium spp. from the Gulf of Maine using molecular probes. Deep-Sea Research II 52: 2467-2490. awards_0_award_nid=54876 awards_0_award_number=NA06NOS4780245 (GOMTOX) awards_0_data_url=https://grantsonline.rdc.noaa.gov/flows/publicSearch/showAwardDetails.do?awdNum=NA06NOS4780245 awards_0_funder_name=National Oceanic and Atmospheric Administration awards_0_funding_acronym=NOAA awards_0_funding_source_nid=352 awards_1_award_nid=54898 awards_1_award_number=1P50-ES01274201 awards_1_funder_name=National Institute of Environmental Health Sciences awards_1_funding_acronym=NIEHS awards_1_funding_source_nid=388 awards_2_award_nid=54916 awards_2_award_number=OCE-0430724 awards_2_data_url=http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=0430724 awards_2_funder_name=NSF Division of Ocean Sciences awards_2_funding_acronym=NSF OCE awards_2_funding_source_nid=355 awards_2_program_manager=Donald L. Rice awards_2_program_manager_nid=51467 awards_3_award_nid=54935 awards_3_award_number=NA160P2785 (MERHAB) awards_3_funder_name=National Oceanic and Atmospheric Administration awards_3_funding_acronym=NOAA awards_3_funding_source_nid=352 cdm_data_type=Other comment=ALEX/Gulf of Maine Multi year bottle data 2003-2010 Version: 21 March 2013 PIs: McGillicuddy, Kosnyrev Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/5824 Easternmost_Easting=-65.728 geospatial_lat_max=45.095833 geospatial_lat_min=40.052 geospatial_lat_units=degrees_north geospatial_lon_max=-65.728 geospatial_lon_min=-71.194833 geospatial_lon_units=degrees_east geospatial_vertical_max=570.0 geospatial_vertical_min=1.0 geospatial_vertical_positive=down geospatial_vertical_units=m infoUrl=https://www.bco-dmo.org/dataset/3358 institution=BCO-DMO instruments_0_acronym=Niskin bottle instruments_0_dataset_instrument_nid=6109 instruments_0_description=A Niskin bottle (a next generation water sampler based on the Nansen bottle) is a cylindrical, non-metallic water collection device with stoppers at both ends. The bottles can be attached individually on a hydrowire or deployed in 12, 24 or 36 bottle Rosette systems mounted on a frame and combined with a CTD. Niskin bottles are used to collect discrete water samples for a range of measurements including pigments, nutrients, plankton, etc. instruments_0_instrument_external_identifier=https://vocab.nerc.ac.uk/collection/L22/current/TOOL0412/ instruments_0_instrument_name=Niskin bottle instruments_0_instrument_nid=413 instruments_0_supplied_name=Niskin bottle instruments_1_acronym=CTD SBE 9 instruments_1_dataset_instrument_nid=6111 instruments_1_description=The Sea-Bird SBE 9 is a type of CTD instrument package. The SBE 9 is the Underwater Unit and is most often combined with the SBE 11 Deck Unit (for real-time readout using conductive wire) when deployed from a research vessel. The combination of the SBE 9 and SBE 11 is called a SBE 911. The SBE 9 uses Sea-Bird's standard modular temperature and conductivity sensors (SBE 3 and SBE 4). The SBE 9 CTD can be configured with auxiliary sensors to measure other parameters including dissolved oxygen, pH, turbidity, fluorometer, altimeter, etc.). Note that in most cases, it is more accurate to specify SBE 911 than SBE 9 since it is likely a SBE 11 deck unit was used. more information from Sea-Bird Electronics instruments_1_instrument_external_identifier=https://vocab.nerc.ac.uk/collection/L05/current/130/ instruments_1_instrument_name=CTD Sea-Bird 9 instruments_1_instrument_nid=488 instruments_1_supplied_name=CTD Sea-Bird 9 instruments_2_acronym=CTD SBE 911plus instruments_2_dataset_instrument_nid=6112 instruments_2_description=The Sea-Bird SBE 911plus is a type of CTD instrument package for continuous measurement of conductivity, temperature and pressure. The SBE 911plus includes the SBE 9plus Underwater Unit and the SBE 11plus Deck Unit (for real-time readout using conductive wire) for deployment from a vessel. The combination of the SBE 9plus and SBE 11plus is called a SBE 911plus. The SBE 9plus uses Sea-Bird's standard modular temperature and conductivity sensors (SBE 3plus and SBE 4). The SBE 9plus CTD can be configured with up to eight auxiliary sensors to measure other parameters including dissolved oxygen, pH, turbidity, fluorescence, light (PAR), light transmission, etc.). more information from Sea-Bird Electronics instruments_2_instrument_external_identifier=https://vocab.nerc.ac.uk/collection/L22/current/TOOL0058/ instruments_2_instrument_name=CTD Sea-Bird SBE 911plus instruments_2_instrument_nid=591 instruments_2_supplied_name=CTD Sea-Bird SBE 911plus instruments_3_acronym=Bran Luebbe AA3 AutoAnalyzer instruments_3_dataset_instrument_nid=6110 instruments_3_description=Bran Luebbe AA3 AutoAnalyzer See the description from the manufacturer. instruments_3_instrument_external_identifier=https://vocab.nerc.ac.uk/collection/L05/current/LAB04/ instruments_3_instrument_name=Bran Luebbe AA3 AutoAnalyzer instruments_3_instrument_nid=700 instruments_3_supplied_name=Bran Luebbe AA3 AutoAnalyzer keywords_vocabulary=GCMD Science Keywords metadata_source=https://www.bco-dmo.org/api/dataset/3358 Northernmost_Northing=45.095833 param_mapping={'3358': {'lat': 'master - latitude', 'depth_nominal': 'flag - depth', 'lon': 'master - longitude'}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/3358/parameters people_0_affiliation=Woods Hole Oceanographic Institution people_0_affiliation_acronym=WHOI people_0_person_name=Dennis J. McGillicuddy people_0_person_nid=50429 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=Woods Hole Oceanographic Institution people_1_affiliation_acronym=WHOI people_1_person_name=Ms Olga Kosnyrev people_1_person_nid=51238 people_1_role=Co-Principal Investigator people_1_role_type=originator people_2_affiliation=Woods Hole Oceanographic Institution people_2_affiliation_acronym=WHOI people_2_person_name=Ms Olga Kosnyrev people_2_person_nid=51238 people_2_role=Contact people_2_role_type=related people_3_affiliation=Woods Hole Oceanographic Institution people_3_affiliation_acronym=WHOI BCO-DMO people_3_person_name=Stephen R. Gegg people_3_person_nid=50910 people_3_role=BCO-DMO Data Manager people_3_role_type=related project=ALEX-GoME projects_0_acronym=ALEX-GoME projects_0_description=Investigations of Alexandrium fundyense dynamics in the Gulf of Maine Project Summary Harmful algal blooms, commonly called "red tides" or HABs, are a serious economic and public health problem throughout the world. In the U.S., the most serious HAB problem is paralytic shellfish poisoning (PSP) , a potentially fatal neurological disorder caused by human ingestion of shellfish that accumulate toxins as they feed on dinoflagellates of the genus Alexandrium. These organisms cause human illness and death due to PSP, repeated shellfish harvest quarantines, and the mortality of fish and marine mammals. This phenomenon, which affects thousands of miles of U.S. coastline and numerous fisheries resources, has expanded dramatically in the last two decades, especially in the Gulf of Maine. ECOHAB-GOM is a project that addresses several fundamental issues regarding Alexandrium blooms in the Gulf of Maine: 1) the source of the Alexandrium cells that appear in the fresh water plumes in the western Maine coastal current (WMCC); 2) Alexandrium cell distribution and dynamics in the eastern Maine coastal current (EMCC); and 3) linkages among blooms in the WMCC, the EMCC and on Georges Bank. Utilizing a combination of numerical

Many species experience heterogeneous environments and adapt genetically to local conditions. The extent of such local adaptation depends on a balance between divergent selection and gene flow, but also on other factors such as phenotypic plasticity or the genetic architecture of traits. Here, we explore the role of life history in this process. We develop a quantitative genetics model and run individual-based simulations to contrast the evolution of local adaptation between short- and long-lived species. We show that local adaptation varies with a species' life cycle and how this cycle modulates the scheduling of selection and dispersal among stages. When a longer generation time is associated with more frequent events of selection than dispersal, local adaptation is more pronounced in long-lived than in short-lived species. Contrastingly, if dispersal occurs more frequently than selection, long-lived species evolve weaker local adaptation. Our simulations confirm these findings and fu..., , , # The evolution of local adaptation in long-lived species

https://doi.org/10.5061/dryad.5qfttdzhn

Description of the data and file structure

The Supplementary Data contain Nemo-Age configuration files (that allow to rerun the individual-based simulations), contain simulation data (that are stored at the end of each simulation run), and contain R scripts (to analyze the simulation data and re-build the figures of the manuscript).

Files and variables

File: ini_files.zip

Description:Â The Nemo-Age configuration files (so called ini files; with file extension .ini) allow to re-run the individual-based simulations (Cotto et al. 2020; for download and installation of Nemo-Age see here (opens in new window)). There are two kinds of ini files, one kind of ini file with environmental trait contributions being present (folder with_env_variance) and one ot...

PCR-based diagnostics has revealed the previously largely unknown Cryptosporidium transmission and infections in high-income countries. This study aimed to determine domestic and imported subtypes of Cryptosporidium species in Norway, evaluate their demographic distribution, and identify potential small outbreaks. Cryptosporidium-positive human faecal samples were obtained from six medical microbiology laboratories between February 2022 and January 2024, together with 22 Cryptosporidium-positive animal samples. Species and subtypes were identified by sequencing PCR products from gp60 and SSU rRNA genes. Most cryptosporidiosis cases occurred during late summer/early autumn, primarily in children and young adults. Of 550 human samples, 359 were successfully characterized molecularly (65%), revealing infection with 10 different Cryptosporidium species. C. parvum occurred in 245 (68%) human isolates with IIa and IId being major allele families, with distinct regional distribution patterns of common subtypes. A kindergarten outbreak with 5 cases was due to C. parvum IIaA14G1R1. C. mortiferum was identified in 33 (9.2%) human cases of which 24 were known to be of domestic origin, making it the second most common species in human autochthonous cases in Norway. All C. mortiferum isolates were of the same genotype; XIVaA20G2T1, including 13 cases from a suspected small outbreak in Trøndelag. C. hominis occurred in 68 typed cases (19%), but mostly in infections acquired abroad, with allele families Ib and If occurring most often. In conclusion, this study of recent Cryptosporidium spp. and subtypes in Norway, highlights the predominance of C. parvum and the emergence of C. mortiferum among autochthonous cases.

Time use survey (TUS) Purpose and brief description The time use survey tries to sketch an as precise picture as possible about the every-day activities of people. In a time use survey, the respondents are asked to record all their activities and their times. Furthermore, additional information about the activities is also asked, such as with whom and where the respondent was.The time use survey tries to sketch an as precise picture as possible about the every-day activities of people. In a time use survey, the respondents are asked to record all their activities and their times. Furthermore, additional information about the activities is also asked, such as with whom and where the respondent was. Statbel carried out this survey in 1999, 2005 and 2013. Population Members of private households where at least one person is in the age group 15-76. Only individuals aged 10 or older are interviewed. Sample frame Demographic data from the National Register. Data collection method In the past, households were visited by an interviewer who gave them instructions about the survey. The members of the household were asked to record their activities in a diary during 2 specific days (one weekday and one weekend day). In the next survey, the data will be collected via a digital platform, composed of a web application and a smartphone application. The fieldwork period is not yet known. Sample size In 1999, 8,382 respondents aged 12 or older from 4,275 households registered their time use for two days. In 2005, there were 6,400 respondents aged 12 or older from 3,474 households. Finally, there were 5,559 respondents aged 10 or older from 2,744 households in 2013. Response rate The response rate amounted to 22.1% in 2013. Periodicity From 2030 onwards, this survey will be part of the IESS (Integrated European Social Statistics) and will be organised every 10 years for Eurostat. Release calendar The results are available at the latest 15 months after the end of the data collection. The most recent results are those of 2013. Definitions A household consists either of a single person, usually living alone, or of two or more persons who, whether or not related to one another by kinship, usually live in one and the same dwelling and live there together. The most common way to present time use data is by using three parameters: The duration per respondent (dpr.): this is the average time spent on a given activity in a given period, calculated for all participants to the research (respondents).this is the average time spent on a given activity in a given period, calculated for all participants to the research (respondents). The participation rate (part.): this is the percentage of respondents who performed a given activity in a given time span. The duration per participant (dpp.): this is the average time spent on a given activity in a given time span, calculated for all participants to the activity. The given period is always a registration day (24h). These three parameters are not independent of each other. The duration per respondent is the product of the duration per participant and the participation rate (number between 0 and 1 expressed as a percentage): Duration per respondent = duration per participant x participation rate This rule holds as far as one looks at the parameters for the registration days (Monday to Sunday) separately, but does not hold for the constructed average weekday and weekend day because we only have the registration of one particular weekday and one particular weekend day per respondent. The parameters for the average weekday and the average weekend day are estimates, taking into account the number of respondents who filled in a particular day for Monday to Friday for the average weekday and for Saturday and Sunday for the average weekend day, respectively. A weighting procedure minimises the deviation in the relationship between duration per respondent, participation rate and duration per participant. An example An example could help to interpret the results: In a weekday the respondents spent on average 2 h 44 on the activity 'work' (=duration per respondent). However, not all participants to the survey worked on the weekdays they kept their diaries. 37.2% of respondents effectively performed the activity 'work' on the recorded weekdays (= participation rate). Respondents who effectively worked on the recorded weekdays spent an average of 7 h 21 on the activity 'work' on a weekday (= duration per participant). Duration per respondent = duration per participant x participation rate 2H44’ = 7h21’ x 37.2% HETUS guidelines Eurostat provides guidelines to carry out the time use survey. They are available here. Reports and articles Technical report of the 2013 Belgian Time-Use Survey SourceTM in opdracht van EUROSTAT SOURCE™ (Software Outreach and Redefinition to Collect E-data through MOTUS) is a project coordinated by Statbel in collaboration with Destatis (the national statistical institute of German

Abstract

The Master List of Schools is a record of all schools in South Africa. The data forms part of the national Education Management Information Systems (EMIS) database used to inform education policymakers and managers in the Department of Basic Education (DBE) and the Provincial education departments, as well as to provide valuable information to external stakeholders. The list is maintained by provincial departments and regularly sent to DBE for updating. A key function of the master list is to uniquely identify each school in the country through a school identifier called the EMIS number. Additionally, the list contains data on school quintiles - categories (quintiles) based on the socioeconomic status of the community in which the school is situated. Analyses comparing schools' performance often use school quintiles as control measures for socioeconomic status, to take into account the effect of, for example, poor infrastructure, shortage of materials and deprived home backgrounds on school performance. There are also other basic data fields in the school master list that could provide the means to answer some of the most frequently asked questions about learner enrolment, teachers and learner-teacher ratio of schools. It is a useful dataset for education planners and researchers and is even widely used in the private sector by those who regularly deal with schools.

Geographic coverage

The data has national coverage

Analysis unit

Individuals and institutions

Universe

The survey covers all schools (ordinary and special needs) in South Africa, both public and independent.

Kind of data

Administrative records and survey data

Mode of data collection

Other

Research instrument

Data from the SNAP survey and ANA that are used to compile the Master List of Schools is collected with a survey questionnaire and educator forms. The principle completes the survey questionnaire and each educator (both state paid and other) in each school completes an educator form. Schools record their EMIS number provided by the DBE on the questionnaire and form for identification.

Data appraisal

The 2023 series only includes data for quarter 2 and quarter 3. The GIS coordinates for schools in the Eastern Cape are incorrectly entered in the original data from the DBE. The data entered in the GIS_long variable is incorrectly entered into the GIS_lat variable. This issue only occurs for schools in the Eastern Cape (EC), all other GIS coordinates for all the other provinces is correct. Therefore, for geospatial analysis, users can swap the GIS coordiate data only for the Eastern Cape.

Inequalities in noise will affect urban wildlife https://doi.org/10.5061/dryad.s4mw6m998 Part I: Spatial Analysis of Noise Distribution across 83 U.S. cities File name: HOLC_Noise_City_Results.csv Date completed: 15 December 2021 Spatial dataset: U.S. Department of Transportation, National Transportation Noise Map 2018 Software used in analysis: ArcGIS Desktop v. 10.7 Part II: Literature review of papers published between 1990 and 23 June 2021 that focus on the impacts of noise pollution to urban wildlife. File name: Urban_noise_wildlife_literature_review.csv Date compiled: 23 June 2021 Search engine: Thompson’s ISI Web of Science Search terms (TS=(WILDLIFE OR ANIMAL OR MAMMAL OR REPTILE OR AMPHIBIAN OR BIRD OR FISH OR INVERTEBRATE) AND TS=(NOISE OR SONAR) AND TS=(CITY OR *URBAN OR METROPOLITAN)). Data collection procedure: To assess the effects of noise on wildlife in urban environments, we conducted a literature review using Thompson’s ISI Web of Science and adapting the methods of Shannon et al. (2016). We adjusted Shannon et al. (2016) search criteria to include urban phrases, resulting in the following search terms (TS=(WILDLIFE OR ANIMAL OR MAMMAL OR REPTILE OR AMPHIBIAN OR BIRD OR FISH OR INVERTEBRATE) AND TS=(NOISE OR SONAR) AND TS=(CITY OR *URBAN OR METROPOLITAN)). We only selected papers published between 1990 and 23 June 2021 (i.e., the date we conducted our search) within the ISI Web of Science categories of ‘Acoustics’, ‘Zoology’, ‘Ecology’, ‘Environmental Sciences’, ‘Ornithology’, ‘Biodiversity Conservation’, ‘Evolutionary Biology’, and ‘Marine Freshwater Biology’. This returned 691 peer-reviewed papers, which we filtered so only empirical studies focused on documenting the effects of anthropogenic noise on wildlife in urban or suburban ecosystems or the effects of urban noise on wildlife in rural environments were included in the final data set. We excluded reviews, meta-analyses, methods papers, and research that took place outside of urban or suburban areas where the noise was not explicitly denoted as urban (e.g., omitted studies that measured traffic noise by parks and reserves in rural areas). For the 241 articles previously analyzed in Shannon et al. (2016), one of our authors reviewed each paper to determine which studies were focused on urban noise. We also verified the noise levels that caused a significant biological response, noting each noise level if multiple responses were recorded. For any new articles published since the Shannon et al. (2016) dataset or those published between 1990 and 2013 but not reviewed by Shannon et al. (2016) (n = 96), two of our authors reviewed each paper to first determine which studies met our criteria and then compiled data on a number of variables of interest, including the noise levels and their resulting biological responses that were statistically significant. For this subset of papers, one author was randomly assigned a list of papers and then a second author was randomly assigned to assess the accuracy of the data collected by the first author. Any discrepancies were discussed as a group until an agreement was reached. Noise categories (environmental, transportation, industrial, multiple, other) were chosen for each paper by noting the explicitly stated source or description of urban noise described in the methodology. Noise levels and their units were reported for each paper, with only noise levels reported in decibels (dB) being used in data analysis. We recorded the sound metric used (i.e., SPL, SPL Max, Leq) for each paper and also recorded the weightings for each noise level. ## Description of the data and file structure Part I: Spatial Analysis of Noise Dataset Variables and Definitions Spreadsheet name: HOLC_Noise_City_Results HOLC_GRADE: HOLC redlining grade (grades A, B, C, D) COUNT: Count of pixels with non zero noise values for a given HOLC grade and city AREA: Area covered by pixels with non zero noise values for a given HOLC grade and city MIN: Minimum pixel noise value for a given HOLC grade and city MAX: Maximum pixel noise value for a given HOLC grade and city RANGE: Pixel noise value range for a given HOLC grade and city MEAN: Mean of pixel noise values for a given HOLC grade and city STD: Standard Deviation of pixel noise values for a given HOLC grade and city SUM: Sum of pixel noise values for a given HOLC grade and city VARIETY: The number of unique values for a given HOLC grade and city MAJORITY: The most frequently occurring value for a given HOLC grade and city MINORITY: The least frequently occurring value for a given HOLC grade and city MEDIAN: Median of pixel noise values for a given HOLC grade and city TOT_HOLC_AREA_SQM: total area in square meters covered by the HOLC grade in given city N_median: N measure of excess noise where N = (grade area with noise pixels * median noise per grade)/total area in grade city: city where analysis was performed stat...

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Abstract

The Master List of Schools is a record of all schools in South Africa. The data forms part of the national Education Management Information Systems (EMIS) database used to inform education policymakers and managers in the Department of Basic Education (DBE) and the Provincial education departments, as well as to provide valuable information to external stakeholders. The list is maintained by provincial departments and regularly sent to DBE for updating. A key function of the master list is to uniquely identify each school in the country through a school identifier called the EMIS number. Additionally, the list contains data on school quintiles - categories (quintiles) based on the socioeconomic status of the community in which the school is situated. Analyses comparing schools' performance often use school quintiles as control measures for socioeconomic status, to take into account the effect of, for example, poor infrastructure, shortage of materials and deprived home backgrounds on school performance. There are also other basic data fields in the school master list that could provide the means to answer some of the most frequently asked questions about learner enrolment, teachers and learner-teacher ratio of schools. It is a useful dataset for education planners and researchers and is even widely used in the private sector by those who regularly deal with schools.

Geographic coverage

The data has national coverage

Analysis unit

Individuals and institutions

Universe

The survey covers all schools (ordinary and special needs) in South Africa, both public and independent.

Kind of data

Administrative records and survey data

Mode of data collection

Other

Research instrument

Data from the SNAP survey and ANA that are used to compile the Master List of Schools is collected with a survey questionnaire and educator forms. The principle completes the survey questionnaire and each educator (both state paid and other) in each school completes an educator form. Schools record their EMIS number provided by the DBE on the questionnaire and form for identification.

Data appraisal

The 2021 series only includes data for quarter 1 and quarter 2.

Context

Our target was to predict gender, age and emotion from audio. We found audio labeled datasets on Mozilla and RAVDESS. So by using R programming language 20 statistical features were extracted and then after adding the labels these datasets were formed. Audio files were collected from "Mozilla Common Voice" and “Ryerson AudioVisual Database of Emotional Speech and Song (RAVDESS)”.

Content

Datasets contains 20 feature columns and 1 column for denoting the label. The 20 statistical features were extracted through the Frequency Spectrum Analysis using R programming Language. They are: 1) meanfreq - The mean frequency (in kHz) is a pitch measure, that assesses the center of the distribution of power across frequencies. 2) sd - The standard deviation of frequency is a statistical measure that describes a dataset’s dispersion relative to its mean and is calculated as the variance’s square root. 3) median - The median frequency (in kHz) is the middle number in the sorted, ascending, or descending list of numbers. 4) Q25 - The first quartile (in kHz), referred to as Q1, is the median of the lower half of the data set. This means that about 25 percent of the data set numbers are below Q1, and about 75 percent are above Q1. 5) Q75 - The third quartile (in kHz), referred to as Q3, is the central point between the median and the highest distributions. 6) IQR - The interquartile range (in kHz) is a measure of statistical dispersion, equal to the difference between 75th and 25th percentiles or between upper and lower quartiles. 7) skew - The skewness is the degree of distortion from the normal distribution. It measures the lack of symmetry in the data distribution. 8) kurt - The kurtosis is a statistical measure that determines how much the tails of distribution vary from the tails of a normal distribution. It is actually the measure of outliers present in the data distribution. 9) sp.ent - The spectral entropy is a measure of signal irregularity that sums up the normalized signal’s spectral power. 10) sfm - The spectral flatness or tonality coefficient, also known as Wiener entropy, is a measure used for digital signal processing to characterize an audio spectrum. Spectral flatness is usually measured in decibels, which, instead of being noise-like, offers a way to calculate how tone-like a sound is. 11) mode - The mode frequency is the most frequently observed value in a data set. 12) centroid - The spectral centroid is a metric used to describe a spectrum in digital signal processing. It means where the spectrum’s center of mass is centered. 13) meanfun - The meanfun is the average of the fundamental frequency measured across the acoustic signal. 14) minfun - The minfun is the minimum fundamental frequency measured across the acoustic signal 15) maxfun - The maxfun is the maximum fundamental frequency measured across the acoustic signal. 16) meandom - The meandom is the average of dominant frequency measured across the acoustic signal. 17) mindom - The mindom is the minimum of dominant frequency measured across the acoustic signal. 18) maxdom - The maxdom is the maximum of dominant frequency measured across the acoustic signal 19) dfrange - The dfrange is the range of dominant frequency measured across the acoustic signal. 20) modindx - the modindx is the modulation index, which calculates the degree of frequency modulation expressed numerically as the ratio of the frequency deviation to the frequency of the modulating signal for a pure tone modulation.

Acknowledgements

Gender and Age Audio Data Souce: Link: https://commonvoice.mozilla.org/en Emotion Audio Data Souce: Link : https://smartlaboratory.org/ravdess/

Analysis of ‘Missing Migrants Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/jmataya/missingmigrants on 14 February 2022.

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

About the Missing Migrants Data

This data is sourced from the International Organization for Migration. The data is part of a specific project called the Missing Migrants Project which tracks deaths of migrants, including refugees , who have gone missing along mixed migration routes worldwide. The research behind this project began with the October 2013 tragedies, when at least 368 individuals died in two shipwrecks near the Italian island of Lampedusa. Since then, Missing Migrants Project has developed into an important hub and advocacy source of information that media, researchers, and the general public access for the latest information.

Where is the data from?

Missing Migrants Project data are compiled from a variety of sources. Sources vary depending on the region and broadly include data from national authorities, such as Coast Guards and Medical Examiners; media reports; NGOs; and interviews with survivors of shipwrecks. In the Mediterranean region, data are relayed from relevant national authorities to IOM field missions, who then share it with the Missing Migrants Project team. Data are also obtained by IOM and other organizations that receive survivors at landing points in Italy and Greece. In other cases, media reports are used. IOM and UNHCR also regularly coordinate on such data to ensure consistency. Data on the U.S./Mexico border are compiled based on data from U.S. county medical examiners and sheriff’s offices, as well as media reports for deaths occurring on the Mexico side of the border. Estimates within Mexico and Central America are based primarily on media and year-end government reports. Data on the Bay of Bengal are drawn from reports by UNHCR and NGOs. In the Horn of Africa, data are obtained from media and NGOs. Data for other regions is drawn from a combination of sources, including media and grassroots organizations. In all regions, Missing Migrants Projectdata represents minimum estimates and are potentially lower than in actuality.

Updated data and visuals can be found here: https://missingmigrants.iom.int/

Who is included in Missing Migrants Project data?

IOM defines a migrant as any person who is moving or has moved across an international border or within a State away from his/her habitual place of residence, regardless of

  (1) the person’s legal status; 
  (2) whether the movement is voluntary or involuntary; 
  (3) what the causes for the movement are; or 
  (4) what the length of the stay is.[1]

Missing Migrants Project counts migrants who have died or gone missing at the external borders of states, or in the process of migration towards an international destination. The count excludes deaths that occur in immigration detention facilities, during deportation, or after forced return to a migrant’s homeland, as well as deaths more loosely connected with migrants’ irregular status, such as those resulting from labour exploitation. Migrants who die or go missing after they are established in a new home are also not included in the data, so deaths in refugee camps or housing are excluded. This approach is chosen because deaths that occur at physical borders and while en route represent a more clearly definable category, and inform what migration routes are most dangerous. Data and knowledge of the risks and vulnerabilities faced by migrants in destination countries, including death, should not be neglected, rather tracked as a distinct category.

How complete is the data on dead and missing migrants?

Data on fatalities during the migration process are challenging to collect for a number of reasons, most stemming from the irregular nature of migratory journeys on which deaths tend to occur. For one, deaths often occur in remote areas on routes chosen with the explicit aim of evading detection. Countless bodies are never found, and rarely do these deaths come to the attention of authorities or the media. Furthermore, when deaths occur at sea, frequently not all bodies are recovered - sometimes with hundreds missing from one shipwreck - and the precise number of missing is often unknown. In 2015, over 50 per cent of deaths recorded by the Missing Migrants Project refer to migrants who are presumed dead and whose bodies have not been found, mainly at sea.

Data are also challenging to collect as reporting on deaths is poor, and the data that does exist are highly scattered. Few official sources are collecting data systematically. Many counts of death rely on media as a source. Coverage can be spotty and incomplete. In addition, the involvement of criminal actors in incidents means there may be fear among survivors to report deaths and some deaths may be actively covered-up. The irregular immigration status of many migrants, and at times their families as well, also impedes reporting of missing persons or deaths.

The varying quality and comprehensiveness of data by region in attempting to estimate deaths globally may exaggerate the share of deaths that occur in some regions, while under-representing the share occurring in others.

What can be understood through this data?

The available data can give an indication of changing conditions and trends related to migration routes and the people travelling on them, which can be relevant for policy making and protection plans. Data can be useful to determine the relative risks of irregular migration routes. For example, Missing Migrants Project data show that despite the increase in migrant flows through the eastern Mediterranean in 2015, the central Mediterranean remained the more deadly route. In 2015, nearly two people died out of every 100 travellers (1.85%) crossing the Central route, as opposed to one out of every 1,000 that crossed from Turkey to Greece (0.095%). From the data, we can also get a sense of whether groups like women and children face additional vulnerabilities on migration routes.

However, it is important to note that because of the challenges in data collection for the missing and dead, basic demographic information on the deceased is rarely known. Often migrants in mixed migration flows do not carry appropriate identification. When bodies are found it may not be possible to identify them or to determine basic demographic information. In the data compiled by Missing Migrants Project, sex of the deceased is unknown in over 80% of cases. Region of origin has been determined for the majority of the deceased. Even this information is at times extrapolated based on available information – for instance if all survivors of a shipwreck are of one origin it was assumed those missing also came from the same region.

The Missing Migrants Project dataset includes coordinates for where incidents of death took place, which indicates where the risks to migrants may be highest. However, it should be noted that all coordinates are estimates.

Why collect data on missing and dead migrants?

By counting lives lost during migration, even if the result is only an informed estimate, we at least acknowledge the fact of these deaths. What before was vague and ill-defined is now a quantified tragedy that must be addressed. Politically, the availability of official data is important. The lack of political commitment at national and international levels to record and account for migrant deaths reflects and contributes to a lack of concern more broadly for the safety and well-being of migrants, including asylum-seekers. Further, it drives public apathy, ignorance, and the dehumanization of these groups.

Data are crucial to better understand the profiles of those who are most at risk and to tailor policies to better assist migrants and prevent loss of life. Ultimately, improved data should contribute to efforts to better understand the causes, both direct and indirect, of fatalities and their potential links to broader migration control policies and practices.

Counting and recording the dead can also be an initial step to encourage improved systems of identification of those who die. Identifying the dead is a moral imperative that respects and acknowledges those who have died. This process can also provide a some sense of closure for families who may otherwise be left without ever knowing the fate of missing loved ones.

Identification and tracing of the dead and missing

As mentioned above, the challenge remains to count the numbers of dead and also identify those counted. Globally, the majority of those who die during migration remain unidentified. Even in cases in which a body is found identification rates are low. Families may search for years or a lifetime to find conclusive news of their loved one. In the meantime, they may face psychological, practical, financial, and legal problems.

Ultimately Missing Migrants Project would like to see that every unidentified body, for which it is possible to recover, is adequately “managed”, analysed and tracked to ensure proper documentation, traceability and dignity. Common forensic protocols and standards should be agreed upon, and used within and between States. Furthermore, data relating to the dead and missing should be held in searchable and open databases at local, national and international levels to facilitate identification.

For more in-depth analysis and discussion of the numbers of missing and dead migrants around the world, and the challenges involved in identification and tracing, read our two reports on the issue, Fatal Journeys: Tracking Lives Lost during Migration (2014) and Fatal Journeys Volume 2, Identification and Tracing of Dead and Missing Migrants

Content

The data set records

Which county has the most Facebook users?

              There are more than 378 million Facebook users in India alone, making it the leading country in terms of Facebook audience size. To put this into context, if India’s Facebook audience were a country then it would be ranked third in terms of largest population worldwide. Apart from India, there are several other markets with more than 100 million Facebook users each: The United States, Indonesia, and Brazil with 193.8 million, 119.05 million, and 112.55 million Facebook users respectively.

              Facebook – the most used social media

              Meta, the company that was previously called Facebook, owns four of the most popular social media platforms worldwide, WhatsApp, Facebook Messenger, Facebook, and Instagram. As of the third quarter of 2021, there were around 3,5 billion cumulative monthly users of the company’s products worldwide. With around 2.9 billion monthly active users, Facebook is the most popular social media worldwide. With an audience of this scale, it is no surprise that the vast majority of Facebook’s revenue is generated through advertising.

              Facebook usage by device
              As of July 2021, it was found that 98.5 percent of active users accessed their Facebook account from mobile devices. In fact, almost 81.8 percent of Facebook audiences worldwide access the platform only via mobile phone. Facebook is not only available through mobile browser as the company has published several mobile apps for users to access their products and services. As of the third quarter 2021, the four core Meta products were leading the ranking of most downloaded mobile apps worldwide, with WhatsApp amassing approximately six billion downloads.

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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.