The Best Management Practices Statistical Estimator (BMPSE) version 1.2.0 was developed by the U.S. Geological Survey (USGS), in cooperation with the Federal Highway Administration (FHWA) Office of Project Delivery and Environmental Review to provide planning-level information about the performance of structural best management practices for decision makers, planners, and highway engineers to assess and mitigate possible adverse effects of highway and urban runoff on the Nation's receiving waters (Granato 2013, 2014; Granato and others, 2021). The BMPSE was assembled by using a Microsoft Access® database application to facilitate calculation of BMP performance statistics. Granato (2014) developed quantitative methods to estimate values of the trapezoidal-distribution statistics, correlation coefficients, and the minimum irreducible concentration (MIC) from available data. Granato (2014) developed the BMPSE to hold and process data from the International Stormwater Best Management Practices Database (BMPDB, www.bmpdatabase.org). Version 1.0 of the BMPSE contained a subset of the data from the 2012 version of the BMPDB; the current version of the BMPSE (1.2.0) contains a subset of the data from the December 2019 version of the BMPDB. Selected data from the BMPDB were screened for import into the BMPSE in consultation with Jane Clary, the data manager for the BMPDB. Modifications included identifying water quality constituents, making measurement units consistent, identifying paired inflow and outflow values, and converting BMPDB water quality values set as half the detection limit back to the detection limit. Total polycyclic aromatic hydrocarbons (PAH) values were added to the BMPSE from BMPDB data; they were calculated from individual PAH measurements at sites with enough data to calculate totals. The BMPSE tool can sort and rank the data, calculate plotting positions, calculate initial estimates, and calculate potential correlations to facilitate the distribution-fitting process (Granato, 2014). For water-quality ratio analysis the BMPSE generates the input files and the list of filenames for each constituent within the Graphical User Interface (GUI). The BMPSE calculates the Spearman’s rho (ρ) and Kendall’s tau (τ) correlation coefficients with their respective 95-percent confidence limits and the probability that each correlation coefficient value is not significantly different from zero by using standard methods (Granato, 2014). If the 95-percent confidence limit values are of the same sign, then the correlation coefficient is statistically different from zero. For hydrograph extension, the BMPSE calculates ρ and τ between the inflow volume and the hydrograph-extension values (Granato, 2014). For volume reduction, the BMPSE calculates ρ and τ between the inflow volume and the ratio of outflow to inflow volumes (Granato, 2014). For water-quality treatment, the BMPSE calculates ρ and τ between the inflow concentrations and the ratio of outflow to inflow concentrations (Granato, 2014; 2020). The BMPSE also calculates ρ between the inflow and the outflow concentrations when a water-quality treatment analysis is done. The current version (1.2.0) of the BMPSE also has the option to calculate urban-runoff quality statistics from inflows to BMPs by using computer code developed for the Highway Runoff Database (Granato and Cazenas, 2009;Granato, 2019). Granato, G.E., 2013, Stochastic empirical loading and dilution model (SELDM) version 1.0.0: U.S. Geological Survey Techniques and Methods, book 4, chap. C3, 112 p., CD-ROM https://pubs.usgs.gov/tm/04/c03 Granato, G.E., 2014, Statistics for stochastic modeling of volume reduction, hydrograph extension, and water-quality treatment by structural stormwater runoff best management practices (BMPs): U.S. Geological Survey Scientific Investigations Report 2014–5037, 37 p., http://dx.doi.org/10.3133/sir20145037. Granato, G.E., 2019, Highway-Runoff Database (HRDB) Version 1.1.0: U.S. Geological Survey data release, https://doi.org/10.5066/P94VL32J. Granato, G.E., and Cazenas, P.A., 2009, Highway-Runoff Database (HRDB Version 1.0)--A data warehouse and preprocessor for the stochastic empirical loading and dilution model: Washington, D.C., U.S. Department of Transportation, Federal Highway Administration, FHWA-HEP-09-004, 57 p. https://pubs.usgs.gov/sir/2009/5269/disc_content_100a_web/FHWA-HEP-09-004.pdf Granato, G.E., Spaetzel, A.B., and Medalie, L., 2021, Statistical methods for simulating structural stormwater runoff best management practices (BMPs) with the stochastic empirical loading and dilution model (SELDM): U.S. Geological Survey Scientific Investigations Report 2020–5136, 41 p., https://doi.org/10.3133/sir20205136

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.

In 2021, almost ** percent of respondents from the United States and United Kingdom stated managing between 1PB and * PB of data. Organizations are collecting and storing increasing amounts of data to use for different purposes. Most of the data collected is unstructured data.

Public health-related decision-making on policies aimed at controlling the COVID-19 pandemic outbreak depends on complex epidemiological models that are compelled to be robust and use all relevant available data. This data article provides a new combined worldwide COVID-19 dataset obtained from official data sources with improved systematic measurement errors and a dedicated dashboard for online data visualization and summary. The dataset adds new measures and attributes to the normal attributes of official data sources, such as daily mortality, and fatality rates. We used comparative statistical analysis to evaluate the measurement errors of COVID-19 official data collections from the Chinese Center for Disease Control and Prevention (Chinese CDC), World Health Organization (WHO) and European Centre for Disease Prevention and Control (ECDC). The data is collected by using text mining techniques and reviewing pdf reports, metadata, and reference data. The combined dataset includes complete spatial data such as countries area, international number of countries, Alpha-2 code, Alpha-3 code, latitude, longitude, and some additional attributes such as population. The improved dataset benefits from major corrections on the referenced data sets and official reports such as adjustments in the reporting dates, which suffered from a one to two days lag, removing negative values, detecting unreasonable changes in historical data in new reports and corrections on systematic measurement errors, which have been increasing as the pandemic outbreak spreads and more countries contribute data for the official repositories. Additionally, the root mean square error of attributes in the paired comparison of datasets was used to identify the main data problems. The data for China is presented separately and in more detail, and it has been extracted from the attached reports available on the main page of the CCDC website. This dataset is a comprehensive and reliable source of worldwide COVID-19 data that can be used in epidemiological models assessing the magnitude and timeline for confirmed cases, long-term predictions of deaths or hospital utilization, the effects of quarantine, stay-at-home orders and other social distancing measures, the pandemic’s turning point or in economic and social impact analysis, helping to inform national and local authorities on how to implement an adaptive response approach to re-opening the economy, re-open schools, alleviate business and social distancing restrictions, design economic programs or allow sports events to resume.

Spatial analysis and statistical summaries of the Protected Areas Database of the United States (PAD-US) provide land managers and decision makers with a general assessment of management intent for biodiversity protection, natural resource management, and recreation access across the nation. The PAD-US 3.0 Combined Fee, Designation, Easement feature class (with Military Lands and Tribal Areas from the Proclamation and Other Planning Boundaries feature class) was modified to remove overlaps, avoiding overestimation in protected area statistics and to support user needs. A Python scripted process ("PADUS3_0_CreateVectorAnalysisFileScript.zip") associated with this data release prioritized overlapping designations (e.g. Wilderness within a National Forest) based upon their relative biodiversity conservation status (e.g. GAP Status Code 1 over 2), public access values (in the order of Closed, Restricted, Open, Unknown), and geodatabase load order (records are deliberately organized in the PAD-US full inventory with fee owned lands loaded before overlapping management designations, and easements). The Vector Analysis File ("PADUS3_0VectorAnalysisFile_ClipCensus.zip") associated item of PAD-US 3.0 Spatial Analysis and Statistics ( https://doi.org/10.5066/P9KLBB5D ) was clipped to the Census state boundary file to define the extent and serve as a common denominator for statistical summaries. Boundaries of interest to stakeholders (State, Department of the Interior Region, Congressional District, County, EcoRegions I-IV, Urban Areas, Landscape Conservation Cooperative) were incorporated into separate geodatabase feature classes to support various data summaries ("PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.zip") and Comma-separated Value (CSV) tables ("PADUS3_0SummaryStatistics_TabularData_CSV.zip") summarizing "PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.zip" are provided as an alternative format and enable users to explore and download summary statistics of interest (Comma-separated Table [CSV], Microsoft Excel Workbook [.XLSX], Portable Document Format [.PDF] Report) from the PAD-US Lands and Inland Water Statistics Dashboard ( https://www.usgs.gov/programs/gap-analysis-project/science/pad-us-statistics ). In addition, a "flattened" version of the PAD-US 3.0 combined file without other extent boundaries ("PADUS3_0VectorAnalysisFile_ClipCensus.zip") allow for other applications that require a representation of overall protection status without overlapping designation boundaries. The "PADUS3_0VectorAnalysis_State_Clip_CENSUS2020" feature class ("PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.gdb") is the source of the PAD-US 3.0 raster files (associated item of PAD-US 3.0 Spatial Analysis and Statistics, https://doi.org/10.5066/P9KLBB5D ). Note, the PAD-US inventory is now considered functionally complete with the vast majority of land protection types represented in some manner, while work continues to maintain updates and improve data quality (see inventory completeness estimates at: http://www.protectedlands.net/data-stewards/ ). In addition, changes in protected area status between versions of the PAD-US may be attributed to improving the completeness and accuracy of the spatial data more than actual management actions or new acquisitions. USGS provides no legal warranty for the use of this data. While PAD-US is the official aggregation of protected areas ( https://www.fgdc.gov/ngda-reports/NGDA_Datasets.html ), agencies are the best source of their lands data.

In 2020, the banking sector led in terms of data-driven decision making within organizations, with ** percent of respondents indicating as such. Other noteworthy sectors for data-driven decision making within organizations are insurance and telecom.

Abstract

The harmonized data set on health, created and published by the ERF, is a subset of Iraq Household Socio Economic Survey (IHSES) 2012. It was derived from the household, individual and health modules, collected in the context of the above mentioned survey. The sample was then used to create a harmonized health survey, comparable with the Iraq Household Socio Economic Survey (IHSES) 2007 micro data set.

----> Overview of the Iraq Household Socio Economic Survey (IHSES) 2012:

Iraq is considered a leader in household expenditure and income surveys where the first was conducted in 1946 followed by surveys in 1954 and 1961. After the establishment of Central Statistical Organization, household expenditure and income surveys were carried out every 3-5 years in (1971/ 1972, 1976, 1979, 1984/ 1985, 1988, 1993, 2002 / 2007). Implementing the cooperation between CSO and WB, Central Statistical Organization (CSO) and Kurdistan Region Statistics Office (KRSO) launched fieldwork on IHSES on 1/1/2012. The survey was carried out over a full year covering all governorates including those in Kurdistan Region.

The survey has six main objectives. These objectives are:

1. Provide data for poverty analysis and measurement and monitor, evaluate and update the implementation Poverty Reduction National Strategy issued in 2009.
2. Provide comprehensive data system to assess household social and economic conditions and prepare the indicators related to the human development.
3. Provide data that meet the needs and requirements of national accounts.
4. Provide detailed indicators on consumption expenditure that serve making decision related to production, consumption, export and import.
5. Provide detailed indicators on the sources of households and individuals income.
6. Provide data necessary for formulation of a new consumer price index number.

The raw survey data provided by the Statistical Office were then harmonized by the Economic Research Forum, to create a comparable version with the 2006/2007 Household Socio Economic Survey in Iraq. Harmonization at this stage only included unifying variables' names, labels and some definitions. See: Iraq 2007 & 2012- Variables Mapping & Availability Matrix.pdf provided in the external resources for further information on the mapping of the original variables on the harmonized ones, in addition to more indications on the variables' availability in both survey years and relevant comments.

Geographic coverage

National coverage: Covering a sample of urban, rural and metropolitan areas in all the governorates including those in Kurdistan Region.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey was carried out over a full year covering all governorates including those in Kurdistan Region.

Kind of data

Sample survey data [ssd]

Sampling procedure

----> Design:

Sample size was (25488) household for the whole Iraq, 216 households for each district of 118 districts, 2832 clusters each of which includes 9 households distributed on districts and governorates for rural and urban.

----> Sample frame:

Listing and numbering results of 2009-2010 Population and Housing Survey were adopted in all the governorates including Kurdistan Region as a frame to select households, the sample was selected in two stages: Stage 1: Primary sampling unit (blocks) within each stratum (district) for urban and rural were systematically selected with probability proportional to size to reach 2832 units (cluster). Stage two: 9 households from each primary sampling unit were selected to create a cluster, thus the sample size of total survey clusters was 25488 households distributed on the governorates, 216 households in each district.

----> Sampling Stages:

In each district, the sample was selected in two stages: Stage 1: based on 2010 listing and numbering frame 24 sample points were selected within each stratum through systematic sampling with probability proportional to size, in addition to the implicit breakdown urban and rural and geographic breakdown (sub-district, quarter, street, county, village and block). Stage 2: Using households as secondary sampling units, 9 households were selected from each sample point using systematic equal probability sampling. Sampling frames of each stages can be developed based on 2010 building listing and numbering without updating household lists. In some small districts, random selection processes of primary sampling may lead to select less than 24 units therefore a sampling unit is selected more than once , the selection may reach two cluster or more from the same enumeration unit when it is necessary.

Mode of data collection

Face-to-face [f2f]

Research instrument

----> Preparation:

The questionnaire of 2006 survey was adopted in designing the questionnaire of 2012 survey on which many revisions were made. Two rounds of pre-test were carried out. Revision were made based on the feedback of field work team, World Bank consultants and others, other revisions were made before final version was implemented in a pilot survey in September 2011. After the pilot survey implemented, other revisions were made in based on the challenges and feedbacks emerged during the implementation to implement the final version in the actual survey.

----> Questionnaire Parts:

The questionnaire consists of four parts each with several sections: Part 1: Socio – Economic Data: - Section 1: Household Roster - Section 2: Emigration - Section 3: Food Rations - Section 4: housing - Section 5: education - Section 6: health - Section 7: Physical measurements - Section 8: job seeking and previous job

Part 2: Monthly, Quarterly and Annual Expenditures: - Section 9: Expenditures on Non – Food Commodities and Services (past 30 days). - Section 10 : Expenditures on Non – Food Commodities and Services (past 90 days). - Section 11: Expenditures on Non – Food Commodities and Services (past 12 months). - Section 12: Expenditures on Non-food Frequent Food Stuff and Commodities (7 days). - Section 12, Table 1: Meals Had Within the Residential Unit. - Section 12, table 2: Number of Persons Participate in the Meals within Household Expenditure Other Than its Members.

Part 3: Income and Other Data: - Section 13: Job - Section 14: paid jobs - Section 15: Agriculture, forestry and fishing - Section 16: Household non – agricultural projects - Section 17: Income from ownership and transfers - Section 18: Durable goods - Section 19: Loans, advances and subsidies - Section 20: Shocks and strategy of dealing in the households - Section 21: Time use - Section 22: Justice - Section 23: Satisfaction in life - Section 24: Food consumption during past 7 days

Part 4: Diary of Daily Expenditures: Diary of expenditure is an essential component of this survey. It is left at the household to record all the daily purchases such as expenditures on food and frequent non-food items such as gasoline, newspapers…etc. during 7 days. Two pages were allocated for recording the expenditures of each day, thus the roster will be consists of 14 pages.

Cleaning operations

----> Raw Data:

Data Editing and Processing: To ensure accuracy and consistency, the data were edited at the following stages: 1. Interviewer: Checks all answers on the household questionnaire, confirming that they are clear and correct. 2. Local Supervisor: Checks to make sure that questions has been correctly completed. 3. Statistical analysis: After exporting data files from excel to SPSS, the Statistical Analysis Unit uses program commands to identify irregular or non-logical values in addition to auditing some variables. 4. World Bank consultants in coordination with the CSO data management team: the World Bank technical consultants use additional programs in SPSS and STAT to examine and correct remaining inconsistencies within the data files. The software detects errors by analyzing questionnaire items according to the expected parameter for each variable.

----> Harmonized Data:

• The SPSS package is used to harmonize the Iraq Household Socio Economic Survey (IHSES) 2007 with Iraq Household Socio Economic Survey (IHSES) 2012.
• The harmonization process starts with raw data files received from the Statistical Office.
• A program is generated for each dataset to create harmonized variables.
• Data is saved on the household and individual level, in SPSS and then converted to STATA, to be disseminated.

Response rate

Iraq Household Socio Economic Survey (IHSES) reached a total of 25488 households. Number of households refused to response was 305, response rate was 98.6%. The highest interview rates were in Ninevah and Muthanna (100%) while the lowest rates were in Sulaimaniya (92%).

This collection contains a snapshot of the learning resource metadata from ESIP's Data management Training Clearinghouse (DMTC) associated with the closeout (March 30, 2023) of the Institute of Museum and Library Services funded (Award Number: LG-70-18-0092-18) Development of an Enhanced and Expanded Data Management Training Clearinghouse project. The shared metadata are a snapshot associated with the final reporting date for the project, and the associated data report is also based upon the same data snapshot on the same date.

The materials included in the collection consist of the following:

esip-dev-02.edacnm.org.json.zip - a zip archive containing the metadata for 587 published learning resources as of March 30, 2023. These metadata include all publicly available metadata elements for the published learning resources with the exception of the metadata elements containing individual email addresses (submitter and contact) to reduce the exposure of these data.

statistics.pdf - an automatically generated report summarizing information about the collection of materials in the DMTC Clearinghouse, including both published and unpublished learning resources. This report includes the numbers of published and unpublished resources through time; the number of learning resources within subject categories and detailed subject categories, the dates items assigned to each category were first added to the Clearinghouse, and the most recent data that items were added to that category; the distribution of learning resources across target audiences; and the frequency of keywords within the learning resource collection. This report is based on the metadata for published resourced included in this collection, and preliminary metadata for unpublished learning resources that are not included in the shared dataset.

The metadata fields consist of the following:

    Fieldname
    Description




    abstract_data
    A brief synopsis or abstract about the learning resource


    abstract_format
    Declaration for how the abstract description will be represented.


    access_conditions
    Conditions upon which the resource can be accessed beyond cost, e.g., login required.


    access_cost
    Yes or No choice stating whether othere is a fee for access to or use of the resource.


    accessibililty_features_name
    Content features of the resource, such as accessible media, alternatives and supported enhancements for accessibility.


    accessibililty_summary
    A human-readable summary of specific accessibility features or deficiencies.


    author_names
    List of authors for a resource derived from the given/first and family/last names of the personal author fields by the system


    author_org
    - name
    - name_identifier
    - name_identifier_type



    - Name of organization authoring the learning resource.
    - The unique identifier for the organization authoring the resource.
    - The identifier scheme associated with the unique identifier for the organization authoring the resource.

authors - givenName - familyName - name_identifier - name_identifier_type

    - Given or first name of person(s) authoring the resource.
    - Last or family name of person(s) authoring the resource.
    - The unique identifier for the person(s) authoring the resource.
    - The identifier scheme associated with the unique identifier for the person(s) authoring the resource, e.g., ORCID.



    citation
    Preferred Form of Citation.


    completion_time
    Intended Time to Complete

contact - name - org - email

    - Name of person(s) who has/have been asserted as the contact(s) for the resource in case of questions or follow-up by resource user.
    - Name of organization that has/have been asserted as the contact(s) for the resource in case of questions or follow-up by resource user.
    - (excluded) Contact email address.



    contributor_orgs
    - name
    - name_identifier
    - name_identifier_type
    - type
    - Name of organization that is a secondary contributor to the learningresource. A contributor can also be an individual person.
    - The unique identifier for the organization contributing to the resource.
    - The identifier scheme associated with the unique identifier for the organization contributing to the resource.
    - Type of contribution to the resource made by an organization.


    contributors
    - familyName
    - givenName
    - name_identifier
    - name_identifier_type

• Last or family name of person(s) contributing to the resource. - Given or first name of person(s) contributing to the resource. - The unique identifier for the person(s) contributing to the resource. - The identifier scheme associated with the unique identifier for the person(s) contributing to the resource, e.g., ORCID.

contributors.type

Type of contribution to the resource made by a person.

    created
    The date on which the metadata record was first saved as part of the input workflow.


    creator
    The name of the person creating the MD record for a resource.


    credential_status
    Declaration of whether a credential is offered for comopletion of the resource.

ed_frameworks - name - description - nodes.name

    - The name of the educational framework to which the resource is aligned, if any. An educational framework is a structured description of educational concepts such as a shared curriculum, syllabus or set of learning objectives, or a vocabulary for describing some other aspect of education such as educational levels or reading ability.
    - A description of one or more subcategories of an educational framework to which a resource is associated.
    - The name of a subcategory of an educational framework to which a resource is associated.


    expertise_level
    The skill level targeted for the topic being taught.


    id
    Unique identifier for the MD record generated by the system in UUID format.


    keywords
    Important phrases or words used to describe the resource.


    language_primary
    Original language in which the learning resource being described is published or made available.


    languages_secondary
    Additional languages in which the resource is tranlated or made available, if any.


    license
    A license for use of that applies to the resource, typically indicated by URL.


    locator_data
    The identifier for the learning resource used as part of a citation, if available.


    locator_type
    Designation of citation locatorr type, e.g., DOI, ARK, Handle.


    lr_outcomes
    Descriptions of what knowledge, skills or abilities students should learn from the resource.


    lr_type
    A characteristic that describes the predominant type or kind of learning resource.


    media_type
    Media type of resource.


    modification_date
    System generated date and time when MD record is modified.


    notes
    MD Record Input Notes


    pub_status
    Status of metadata record within the system, i.e., in-process, in-review, pre-pub-review, deprecate-request, deprecated or published.


    published
    Date of first broadcast / publication.


    publisher
    The organization credited with publishing or broadcasting the resource.


    purpose
    The purpose of the resource in the context of education; e.g., instruction, professional education, assessment.


    rating
    The aggregation of input from all user assessments evaluating users' reaction to the learning resource following Kirkpatrick's model of training evaluation.


    ratings
    Inputs from users assessing each user's reaction to the learning resource following Kirkpatrick's model of training evaluation.


    resource_modification_date
    Date in which the resource has last been modified from the original published or broadcast version.


    status
    System generated publication status of the resource w/in the registry as a yes for published or no for not published.


    subject
    Subject domain(s) toward which the resource is targeted. There may be more than one value for this field.


    submitter_email
    (excluded) Email address of person who submitted the resource.


    submitter_name
    Submission Contact Person


    target_audience
    Audience(s) for which the resource is intended.


    title
    The name of the resource.


    url
    URL that resolves to a downloadable version of the learning resource or to a landing page for the resource that contains important contextual information including the direct resolvable link to the resource, if applicable.


    usage_info
    Descriptive information about using the resource, not addressed by the License information field.


    version
    The specific version of the resource, if declared.

The Journal of Physiology and British Journal of Pharmacology jointly published an editorial series in 2011 to improve standards in statistical reporting and data analysis. It is not known whether reporting practices changed in response to the editorial advice. We conducted a cross-sectional analysis of reporting practices in a random sample of research papers published in these journals before (n = 202) and after (n = 199) publication of the editorial advice. Descriptive data are presented. There was no evidence that reporting practices improved following publication of the editorial advice. Overall, 76-84% of papers with written measures that summarized data variability used standard errors of the mean, and 90-96% of papers did not report exact p-values for primary analyses and post-hoc tests. 76-84% of papers that plotted measures to summarize data variability used standard errors of the mean, and only 2-4% of papers plotted raw data used to calculate variability. Of papers that reported p-values between 0.05 and 0.1, 56-63% interpreted these as trends or statistically significant. Implied or gross spin was noted incidentally in papers before (n = 10) and after (n = 9) the editorial advice was published. Overall, poor statistical reporting, inadequate data presentation and spin were present before and after the editorial advice was published. While the scientific community continues to implement strategies for improving reporting practices, our results indicate stronger incentives or enforcements are needed.

MethodsThe objective of this project was to determine the capability of a federated analysis approach using DataSHIELD to maintain the level of results of a classical centralized analysis in a real-world setting. This research was carried out on an anonymous synthetic longitudinal real-world oncology cohort randomly splitted in three local databases, mimicking three healthcare organizations, stored in a federated data platform integrating DataSHIELD. No individual data transfer, statistics were calculated simultaneously but in parallel within each healthcare organization and only summary statistics (aggregates) were provided back to the federated data analyst.Descriptive statistics, survival analysis, regression models and correlation were first performed on the centralized approach and then reproduced on the federated approach. The results were then compared between the two approaches.ResultsThe cohort was splitted in three samples (N1 = 157 patients, N2 = 94 and N3 = 64), 11 derived variables and four types of analyses were generated. All analyses were successfully reproduced using DataSHIELD, except for one descriptive variable due to data disclosure limitation in the federated environment, showing the good capability of DataSHIELD. For descriptive statistics, exactly equivalent results were found for the federated and centralized approaches, except some differences for position measures. Estimates of univariate regression models were similar, with a loss of accuracy observed for multivariate models due to source database variability.ConclusionOur project showed a practical implementation and use case of a real-world federated approach using DataSHIELD. The capability and accuracy of common data manipulation and analysis were satisfying, and the flexibility of the tool enabled the production of a variety of analyses while preserving the privacy of individual data. The DataSHIELD forum was also a practical source of information and support. In order to find the right balance between privacy and accuracy of the analysis, set-up of privacy requirements should be established prior to the start of the analysis, as well as a data quality review of the participating healthcare organization.

This statistic shows the leading uses of cloud technology for data protection in companies worldwide as of 2019. A total of ** percent of survey respondents stated that, in their organization, cloud technology was used for archiving and long term retention.

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.

Software tools used to collect and analyze data. Parentheses for analysis software indicate the tools participants were taught to use as part of their education in research methods and statistics. “Other” responses for data collection software were largely comprised of survey tools (e.g. Survey Monkey, LimeSurvey) and tools for building and running behavioral experiments (e.g. Gorilla, JsPsych). “Other” responses for data analysis software largely consisted of neuroimaging-related tools (e.g. SPM, AFNI).

Learning Goals: • explain importance of data management • identify elements of an organized data sheet • create & manipulate data in a spreadsheet • calculate vital statistics using life tables • collect, manage and analyze data to test hypotheses

About this dataset

Context

The dataset tabulates the population of Malta by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Malta. The dataset can be utilized to understand the population distribution of Malta by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Malta. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Malta.

Key observations

Largest age group (population): Male # 35-39 years (52) | Female # 25-29 years (62). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Malta population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Malta is shown in the following column.
• Population (Female): The female population in the Malta is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Malta for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Malta Population by Gender. You can refer the same here

This dataset provides an in-depth look at the League of Legends Champions Korea (LCK) Spring 2024 season. It includes detailed metrics for players, champions, and matches, meticulously cleaned and organized for easy analysis and modeling.

Data Collection

The data was collected using a combination of manual efforts and automated web scraping tools. Specifically:

Source: Data was gathered from Gol.gg, a well-known platform for League of Legends statistics. Automation: Web scraping was performed using Python libraries like BeautifulSoup and Selenium to extract information on players, matches, and champions efficiently. Focus: The scripts were designed to capture relevant performance metrics for each player and champion used during the Spring 2024 split.

Data Cleaning and Processing

The raw data obtained from web scraping required significant preprocessing to ensure its usability. The following steps were taken:

Handling Raw Data:

Extracted key performance indicators like KDA, Win Rate, Games Played, and Match Durations from the source. Normalized inconsistent formats for metrics such as win rates (e.g., removing %) and durations (e.g., converting MM:SS to total seconds).

Data Cleaning:

Removed duplicate rows and ensured no missing values. Fixed inconsistencies in player and champion names to maintain uniformity. Checked for outliers in numerical metrics (e.g., unrealistically high KDA values).

Data Organization:

Created three separate tables for better data management:

Player Statistics: General player performance metrics like KDA, win rates, and average kills. Champion Statistics: Data on games played, win rates, and KDA for each champion. Match List: Details of each match, including players, champions, and results. Added sequential Player IDs to connect the three datasets, facilitating relational analysis. Date Formatting: Converted all date fields to the DD/MM/YYYY format for consistency. Removed irrelevant time data to focus solely on match dates.

Tools and Libraries Used

The following tools were used throughout the project:

Python: Libraries: Pandas, NumPy for data manipulation; BeautifulSoup, Selenium for web scraping. Visualization: Matplotlib, Seaborn, Plotly for potential analysis. Excel: Consolidated final datasets into a structured Excel file with multiple sheets. Data Validation: Used Python scripts to check for missing data, validate numerical columns, and ensure data consistency. Kaggle Integration: Cleaned datasets and a comprehensive README file were prepared for direct upload to Kaggle.

Applications

This dataset is ready for use in: Exploratory Data Analysis (EDA): Visualize player and champion performance trends across matches. Machine Learning: Develop models to predict match outcomes based on player and champion statistics. Sports Analytics: Gain insights into champion picks, win rates, and individual player strategies.

Acknowledgments

This dataset was made possible by the extensive statistics available on Gol.gg and the use of Python-based web scraping and data cleaning methodologies. It is shared under the CC BY 4.0 License to encourage reuse and collaboration.

Graph and download economic data for Producer Price Index by Commodity: Data Processing and Related Services: Data Management, Information Transformation and Other Services (WPU381103) from Dec 2008 to Sep 2025 about information technology, management, information, processed, services, commodities, PPI, inflation, price index, indexes, price, and USA.

In 2020, the United States led in terms of data-driven decision making within organizations, with ** percent of respondents indicating as such. Other noteworthy countries for data-driven decision making within organizations are Germany and the United Kingdom.