WHOSIS, the WHO Statistical Information System, is an interactive database bringing together core health statistics for the 193 WHO Member States. It comprises more than 100 indicators, which can be accessed by way of a quick search, by major categories, or through user-defined tables. The data can be further filtered, tabulated, charted and downloaded. The data are also published annually in the World Health Statistics Report released in May. The WHO Statistical Information System is the guide to health and health-related epidemiological and statistical information available from the World Health Organization. Most WHO technical programs make statistical information available, and they will be linked from here. Sponsors: WHOSIS is supported by the World Health Organization. Note: The WHO Statistical Information System (WHOSIS) has been incorporated into the Global Health Observatory (GHO) to provide you with more data, more tools, more analysis and more reports.

Figures in scientific publications are critically important because they often show the data supporting key findings. Our systematic review of research articles published in top physiology journals (n = 703) suggests that, as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies. Papers rarely included scatterplots, box plots, and histograms that allow readers to critically evaluate continuous data. Most papers presented continuous data in bar and line graphs. This is problematic, as many different data distributions can lead to the same bar or line graph. The full data may suggest different conclusions from the summary statistics. We recommend training investigators in data presentation, encouraging a more complete presentation of data, and changing journal editorial policies. Investigators can quickly make univariate scatterplots for small sample size studies using our Excel templates.

Statistics of natural scenes are not uniform - their structure varies dramatically from ground to sky. It remains unknown whether these non-uniformities are reflected in the large-scale organization of the early visual system and what benefits such adaptations would confer. Here, by relying on the efficient coding hypothesis, we predict that changes in the structure of receptive fields across visual space increase the efficiency of sensory coding. We show experimentally that, in agreement with our predictions, receptive fields of retinal ganglion cells change their shape along the dorsoventral retinal axis, with a marked surround asymmetry at the visual horizon. Our work demonstrates that, according to principles of efficient coding, the panoramic structure of natural scenes is exploited by the retina across space and cell-types.

Statistics of Institute of Tourism of Spain: Number of trips by form of organisation, according to duration. Annual. National.

The data has been collected through a survey of enterprises with at least 50 persons employed.

The data category covers a group of variables which provide relevant statistical evidence and information about factors driving international sourcing e.g. the impact on the competitiveness, motivations and perceived barriers together with possible employment consequences in the Member State.

There have been four collection rounds:

1. In 2007 the data has been collected on a voluntary basis for 13 countries: Germany, Czechia, Netherlands, Denmark, Spain, Ireland, Italy, Portugal, Finland, Slovenia, Sweden, the United Kingdom and Norway.
2. In 2011 the data has been collected on a voluntary basis for 15 countries: Belgium, Bulgaria, Denmark, Estonia, Ireland, France, Latvia, Lithuania, Netherlands, Portugal, Romania, Finland, Slovakia, Sweden and Norway.
3. In 2018 the data has been collected on a voluntary basis for 16 countries: Austria, Bulgaria, Denmark, Finland, Germany, Hungary, Italy, Latvia, Lithuania, Netherlands, Poland, Portugal, Romania, Slovakia, Sweden and Norway.
4. In 2021 the data has been collected on a voluntary basis for 17 countries: Austria, Bulgaria, Denmark, Finland, France, Germany, Hungary, Ireland, Italy, Latvia, Lithuania, Malta, Netherlands, Poland, Portugal, Sweden and Norway. French data will be published by the end of 2022.

The data focuses on the relocation of core and support business functions of enterprises in the business economy sector, from domestic to abroad and vice versa, as a result of decisions taken by the domestic enterprises.

In summary, the collected indicators are :

• Number of enterprises
• Number of jobs created and jobs lost as a result of international sourcing activity
• Number of persons employed

The dimensions used to describe the International sourcing in the 2021 collection round are:

• Economic activity
• Sourcing status
• Business function
• Sourcing activities
• Type of business partners
• Country destination of international sourcing
• Movement of business functions abroad
• Job creation/job loss identification
• Motivation factors and their level of importance
• Barriers for international sourcing

In the 2021 collection round two new dimensions were include:

• GVC arrangements related to enterprises purchasing and supplying goods and services abroad
• COVID-19 impact on international sourcing, turnover and trade

Turkey Import: Services: Organization of Islamic Cooperation data was reported at 2.575 USD bn in 2022. This records an increase from the previous number of 1.883 USD bn for 2021. Turkey Import: Services: Organization of Islamic Cooperation data is updated yearly, averaging 1.991 USD bn from Dec 2018 (Median) to 2022, with 5 observations. The data reached an all-time high of 2.575 USD bn in 2022 and a record low of 1.334 USD bn in 2020. Turkey Import: Services: Organization of Islamic Cooperation data remains active status in CEIC and is reported by Turkish Statistical Institute. The data is categorized under Global Database’s Turkey – Table TR.JA074: Trade Statistics: Services: by Country Group.

Related article: Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39.

In this dataset:

We present temporally dynamic population distribution data from the Helsinki Metropolitan Area, Finland, at the level of 250 m by 250 m statistical grid cells. Three hourly population distribution datasets are provided for regular workdays (Mon – Thu), Saturdays and Sundays. The data are based on aggregated mobile phone data collected by the biggest mobile network operator in Finland. Mobile phone data are assigned to statistical grid cells using an advanced dasymetric interpolation method based on ancillary data about land cover, buildings and a time use survey. The data were validated by comparing population register data from Statistics Finland for night-time hours and a daytime workplace registry. The resulting 24-hour population data can be used to reveal the temporal dynamics of the city and examine population variations relevant to for instance spatial accessibility analyses, crisis management and planning.

Please cite this dataset as:

Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39. https://doi.org/10.1038/s41597-021-01113-4

Organization of data

The dataset is packaged into a single Zipfile Helsinki_dynpop_matrix.zip which contains following files:

HMA_Dynamic_population_24H_workdays.csv represents the dynamic population for average workday in the study area.

HMA_Dynamic_population_24H_sat.csv represents the dynamic population for average saturday in the study area.

HMA_Dynamic_population_24H_sun.csv represents the dynamic population for average sunday in the study area.

target_zones_grid250m_EPSG3067.geojson represents the statistical grid in ETRS89/ETRS-TM35FIN projection that can be used to visualize the data on a map using e.g. QGIS.

Column names

YKR_ID : a unique identifier for each statistical grid cell (n=13,231). The identifier is compatible with the statistical YKR grid cell data by Statistics Finland and Finnish Environment Institute.

H0, H1 ... H23 : Each field represents the proportional distribution of the total population in the study area between grid cells during a one-hour period. In total, 24 fields are formatted as “Hx”, where x stands for the hour of the day (values ranging from 0-23). For example, H0 stands for the first hour of the day: 00:00 - 00:59. The sum of all cell values for each field equals to 100 (i.e. 100% of total population for each one-hour period)

In order to visualize the data on a map, the result tables can be joined with the target_zones_grid250m_EPSG3067.geojson data. The data can be joined by using the field YKR_ID as a common key between the datasets.

License Creative Commons Attribution 4.0 International.

Related datasets

Järv, Olle; Tenkanen, Henrikki & Toivonen, Tuuli. (2017). Multi-temporal function-based dasymetric interpolation tool for mobile phone data. Zenodo. https://doi.org/10.5281/zenodo.252612

Tenkanen, Henrikki, & Toivonen, Tuuli. (2019). Helsinki Region Travel Time Matrix [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3247564

Russia RU: Pharmaceutical Industry: Total Imports data was reported at 14.330 USD bn in 2021. This records an increase from the previous number of 11.326 USD bn for 2020. Russia RU: Pharmaceutical Industry: Total Imports data is updated yearly, averaging 8.832 USD bn from Dec 1996 (Median) to 2021, with 26 observations. The data reached an all-time high of 14.868 USD bn in 2013 and a record low of 912.058 USD mn in 1999. Russia RU: Pharmaceutical Industry: Total Imports data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Russian Federation – Table RU.OECD.MSTI: Trade Statistics: Non OECD Member: Annual.

In response to Russia's large-scale aggression against Ukraine, the OECD Council decided on 8 March 2022 to immediately suspend the participation of Russia and Belarus in OECD bodies. In view of this decision, the OECD suspended its solicitation of official statistics on R&D from Russian authorities, leading to the absence of more recent R&D statistics for this country in the OECD database. Previously collected and compiled indicators are still available.

The business enterprise sector includes all organisations and enterprises whose main activity is connected with the production of goods and services for sale, including those owned by the state, and private non-profit institutions serving the above-mentioned organisations. In practice however, R&D performed in this sector is carried out mostly by industrial research institutes other than enterprises. This particularity reflects the traditional organisation of Russian R&D.

Headcount data include full-time personnel only, and hence are underestimated, while data in full-time equivalents (FTE) are calculated on the basis of both full-time and part-time personnel. This explains why the FTE data are greater than the headcount data.

New budgetary procedures introduced in 2005 have resulted in items previously classified as GBARD being attributed to other headings and have affected the coverage and breakdown by socio-economic objective.

This report shows monthly numbers of NHS Hospital and Community Health Services (HCHS) staff working in NHS Trusts and CCGs in England (excluding primary care staff). Data are available as headcount and full-time equivalents and for all months from 30 September 2009 onwards. These data are an accurate summary of the validated data extracted from the NHS HR and Payroll system. Additional statistics on staff in NHS Trusts and CCGs and information for NHS Support Organisations and Central Bodies are published each: September (showing June statistics) December (showing September statistics) March (showing December statistics) June (showing March statistics) Quarterly NHS Staff Earnings and monthly NHS Staff Sickness Absence reports and data relating to the General Practice workforce and the Independent Healthcare Provider workforce are also available via the Related Links below. In the next quarterly release of data, September data published in December, we intend to stop publishing the following CSV documents: 1. ‘HCHS staff in NHS Trusts and CCGs in England, Organisation and Job Type CSV’ These data are already available within the ‘HCHS staff in NHS Trusts and CCGs - Staff in Post summary tables’ on the tab ‘Source - Org, SG, grade, AoW’ 2. ‘HCHS staff in NHS Support Organisations and Central Bodies in England, Organisation and Job Type CSV’ These data are already available within the ‘HCHS staff in NHS Support Orgs and Central Bodies’ on the tab ‘Source - Staff Grp, Grade, AoW’ Please let us know if this causes any inconvenience. We welcome feedback on the methodology and tables within this publication. Please email us with your comments and suggestions, clearly stating Monthly HCHS Workforce as the subject heading, via enquiries@nhsdigital.nhs.uk or 0300 303 5678.

This dataset contains about 5 years of analysed observations regarding the degree of convective aggregation, or clumping, across the tropics - these are averaged onto a large-scale grid. There are also additional variables which represent environmental fields (e.g. sea surface temperature from satellite data, or humidity profiles averaged from reanalysis data) averaged onto the same large-scale grid. The main aggregation index is the Simple Convective Aggregation Index (SCAI) originally defined in Tobin et al. 2012, Journal of Climate. The data were created during the main years of CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite data so that they could be compared with vertical cloud profiles from this satellite data, and the results of this analysis appear in Stein et al. 2017, Journal of Climate.

Each file is one year of data (although the year may not be complete).

Each variable is an array: var(nlon, nlat, [nlev], ntime) longitude, latitude, pressure, time are variables in each file units are attributes of each variable (except non-dimensional ones) missing_value is 3.0E20 and is an attribute of each variable

Time is in days since 19790101:00Z and is every 3hours at 00z, 03z, ... The actual temporal frequency of the data is described for each variable below.

The data is for each 10deg X 10deg lat/lon box, 30S-30N (at outer edges of box domain), with each box defined by its centre coordinates and with boxes overlapping each other by 5deg in each direction.

In general, each variable is a spatial average over each box, with the value set to missing if more than 15% of the box is missing data. Exceptions to this are given below. The most important exception is for the brightness temperature data, used in aggregation statistics, which is filled in using neighborhood averaging if no more than 5% of the pixels are missing, but otherwise is considered to be all missing data. The percentage of missing pixels is recorded in 'bt_miss_frac'.

Abstract copyright UK Data Service and data collection copyright owner.

The UIS collects research and development data from more than 200 countries and territories through its biennial research and experimental development survey and partnerships with other statistical organizations, such as Eurostat, Red Iberoamericana de Indicadores de Ciencia y Tecnologia (RICYT) and the Organisation for Economic Co-operation and Development (OECD).

The UNESCO Institute of Statistics (UIS) produces a wide range of indicators on the human and financial resources invested in Research and Development for countries at all stages of development. The Research and Experimental Developments Statistics consist of annual data from 1996 onwards for over 160 countries. The data were collected through the Institute’s survey on R/D statistics in partnership with regional and international organizations, including the Organisation for Economic Co-operation and Development (OECD) and Eurostat.

UNESCO (2017): Research and Experimental Development Statistics (Edition: July 2016). UK Data Service. DOI: http://dx.doi.org/10.5257/unesco/scn/2016-07.

OECD statistics contact: stat.contact@oecd.org

The OECD Secretariat collects a wide range of statistics on businesses and business activity. The Structural Business Statistics by size class dataset is part of the Structural and Demographic Business Statistics (SDBS) database featuring the harmonised data collection of the OECD Statistics and Data Directorate relating to a number of key variables, such as value added, operating surplus, employment, and the number of business units.

Data are broken down to class (4-digit) level of International Standard of Industrial Classification (ISIC Revision 4), and by enterprise size class based on the number of persons employed.

Data cover OECD member and partner countries, non-OECD countries that are members of the European Statistical System who provide data to Eurostat, as well as countries participating in OECD Regional initiatives.

Context

Riga Data Science Club is a non-profit organisation to share ideas, experience and build machine learning projects together. Data Science community should known own data, so this is a dataset about ourselves: our website analytics, social media activity, slack statistics and even meetup transcriptions!

Content

Dataset is split up in several folders by the context: * linkedin - company page visitor, follower and post stats * slack - messaging and member activity * typeform - new member responses * website - website visitors by country, language, device, operating system, screen resolution * youtube - meetup transcriptions

Inspiration

Let's make Riga Data Science Club better! We expect this data to bring lots of insights on how to improve.

"Know your c̶u̶s̶t̶o̶m̶e̶r̶ member" - Explore member interests by analysing sign-up survey (typeform) responses - Explore messaging patterns in Slack to understand how members are retained and when they are lost

Social media intelligence * Define LinkedIn posting strategy based on historical engagement data * Define target user profile based on LinkedIn page attendance data

Website * Define website localisation strategy based on data about visitor countries and languages * Define website responsive design strategy based on data about visitor devices, operating systems and screen resolutions

Have some fun * NLP analysis of meetup transcriptions: word frequencies, question answering, something else?

OECD statistics contact: stat.contact@oecd.org

The International Trade dataset contains predominantly monthly merchandise trade statistics, and associated statistical methodological information, for all OECD member countries and for all non-OECD G20 economies and the EU.

The dataset itself contains international trade statistics measured in billions of US dollars (USD) or Millions of billions of National Currency or Growth rates for: Exports, Imports, Trade Balance. In all cases a lot of effort has been made to ensure that the data are internationally comparable across all countries presented and that all the subjects have good historical time-series’ data to aid with analysis.

Tourist movement on borders: Number of tourists by travel organisation. Annual. National.

Table NameInformation: Subject Series: Estab & Firm Size: Summary Statistics by Legal Form of Organization for the U.S.: 2012ReleaseScheduleThe data in this file are scheduled for release in March 2016.Key TableInformationEC1251SSSZ1 through EC1251SSSZ6 present data by employment and receipt size for establishments and firms, single unit and multiunit firms, and concentration by largest firms for the United States. See Methodology. for additional information on data limitations.UniverseThe universe of this file is all establishments of firms with payroll in business at any time during 2012 and classified in Information (Sector 51).GeographyCoverageThe data are shown at the United States level only.IndustryCoverageThe data are shown for 2- through 7-digit 2012 NAICS codes.Data ItemsandOtherIdentifyingRecordsThis file contains data on:. Establishments. Receipts. Annual payroll. First-quarter payroll. Paid employees.Each record includes an LFO code which represents a specific legal form of organization of establishments.FTP DownloadDownload the entire table athttps://www2.census.gov/econ2012/EC/sector51/EC1251SSSZ7.zipContactInformation. U.S. Census Bureau, Economy Wide Statistics Division. Data User Outreach and Education Staff. Washington, DC 20233-6900. Tel: (800) 242-2184. Tel: (301) 763-5154. ewd.outreach@census.gov. . .For information on economic census geographies, including changes for 2012, see the economic census Help Center..Includes only establishments of firms with payroll. See Table Notes for more information. Data based on the 2012 Economic Census. For method of assignment to categories shown and for information on confidentiality protection, sampling error, nonsampling error, and definitions, see Methodology..Symbols:D - Withheld to avoid disclosing data for individual companies; data are included in higher level totalsN - Not available or not comparableFor a complete list of all economic programs symbols, see the Symbols Glossary.Source: U.S. Census Bureau, 2012 Economic Census.Note: The data in this file are based on the 2012 Economic Census. To maintain confidentiality, the U.S. Census Bureau suppresses data to protect the identity of any business or individual. The census results in this file contain nonsampling error. Data users who create their own estimates using data from this file should cite the U.S. Census Bureau as the source of the original data only. For the full technical documentation, see Methodology link in above headnote.

This publication provides the most timely picture available of people using NHS funded secondary mental health, learning disabilities and autism services in England. These are experimental statistics which are undergoing development and evaluation. This information will be of use to people needing access to information quickly for operational decision making and other purposes. More detailed information on the quality and completeness of these statistics is made available later in our Mental Health Bulletin: Annual Report publication series. • COVID-19 and the production of statistics Due to the coronavirus illness (COVID-19) disruption, it would seem that this is now starting to affect the quality and coverage of some of our statistics, such as an increase in non-submissions for some datasets. We are also starting to see some different patterns in the submitted data. For example, fewer patients are being referred to hospital and more appointments being carried out via phone/telemedicine/email. Therefore, data should be interpreted with care over the COVID-19 period. • Early release of statistics To support the ongoing COVID-19 work, August 2020 monthly statistics were made available early and presented on our supplementary information pages. https://digital.nhs.uk/data-and-information/supplementary-information/2020/provisional-august-2020-mental-health-statistics • Changing existing measures The move to MHSDS version 4.1 from April 2020 has brought with it changes to the dataset; the construction of a number of measures have been changed as a result. Improvements in the methodology of reporting delay of discharge has also resulted in a change in the construction of the measure from the April 2020 publication onwards. Full details of these changes are available in the associated Metadata file. • New measures A number of new measures have been included from the July 2020 publication onwards: • MHS81 Number of Detentions • MHS82 Number of Short Term Orders • MHS83 Number of uses of Section 136 • MHS84 Number of Community Treatment Orders Full details of these are available in the associated Metadata file. • CCG and STP changes A number of changes to NHS organisations were made operationally effective from 1 April 2020. These changes included: 74 former Clinical Commissioning Groups (CCGs) merging to form 18 new CCGs; alterations to commissioning hubs; provider mergers; and the incorporation of Sustainability and Transformation Partnerships (STPs) into the NHS commissioning hierarchy. The Organisation Data Service (ODS) is responsible for publishing organisation and practitioner codes, along with related national policies and standards. A series of ODS data amendments are required to support the introduction of these changes. This would normally result in a number of organisations becoming ‘legally’ closed including the 74 former CCGs. However, to minimise any burden to the NHS during the COVID-19 pandemic and remove any non-critical activity, these organisations remain open within ODS data. ODS aim to both legally and operationally close predecessor organisations involved in April 2020 Reconfiguration on 30 September 2020. Activity may be recorded against either former or current organisations, depending on data providers and processors ability to transition to the new organisation codes at this time. The same activity will not be recorded against both former and current organisations. There is no impact on the statistics presented here as CCG is derived in all cases within this publication.