The average environmental and occupational physiologist may find statistics are difficult to interpret and use since their formal training in statistics is limited. Unfortunately, poor statistical practices can generate erroneous or at least misleading results and distorts the evidence in the scientific literature. These problems are exacerbated when statistics are used as thoughtless ritual that is performed after the data are collected. The situation is worsened when statistics are then treated as strict judgements about the data (i.e., significant versus non-significant) without a thought given to how these statistics were calculated or their practical meaning. We propose that researchers should consider statistics at every step of the research process whether that be the designing of experiments, collecting data, analysing the data or disseminating the results. When statistics are considered as an integral part of the research process, from start to finish, several problematic practices can be mitigated. Further, proper practices in disseminating the results of a study can greatly improve the quality of the literature. Within this review, we have included a number of reminders and statistical questions researchers should answer throughout the scientific process. Rather than treat statistics as a strict rule following procedure we hope that readers will use this review to stimulate a discussion around their current practices and attempt to improve them. The code to reproduce all analyses and figures within the manuscript can be found at https://doi.org/10.17605/OSF.IO/BQGDH.

TEMPO-Online provides the following functions and services: Free access to statistical information.Export of tables in .csv and .xls formats and its printing. What is the content of TEMPO-Online? The National Institute of Statistics offers a statistical database, TEMPO-Online, that gives the possibility to access a large range of information.The content of the above-mentioned database consists of:Approximately 1100 statistical indicators, divided in socio-economical fields and sub-fields; Metadata associated to the statistical indicators (definition, starting and ending year of the time series, the last period of data loading, statistical methodology, the last updating); Detailed indicators at statistical characteristics group and/or sub-group level ( ex. The total number of employees at the end of the year by employee category, activities of the national economy - sections, sexes, areas and counties); Time series starting with 1990 - till today: With a monthly, quarterly, semi-annual and annual frequency; At national level, development region level, county and commune level. Search according to key words The search key words allows the finding of various objects (tables with statistical variables divided on time series). The search will give back results based on the matrix code and on the key words in the title or in the definition of a matrix. The result of the search will show on a list with specific objects. For a key word, one can use the searching section from the menu bar on the left.Tables As a whole, the tables that result following an interrogation have a flexible structure. For instance, the user may select the variables and attributes with the help of the interrogation interface, according to his needs.The user can save the table that results following an interrogation in .csv and .xls formats and its printingNote: in order to access tables at place level (very large), the user has to select each county with the respective places, so that the access be faster and avoid technical blocks.

Website: http://statistici.insse.ro/shop/?lang=en

LScDC Word-Category RIG MatrixApril 2020 by Neslihan Suzen, PhD student at the University of Leicester (ns433@leicester.ac.uk / suzenneslihan@hotmail.com)Supervised by Prof Alexander Gorban and Dr Evgeny MirkesGetting StartedThis file describes the Word-Category RIG Matrix for theLeicester Scientific Corpus (LSC) [1], the procedure to build the matrix and introduces the Leicester Scientific Thesaurus (LScT) with the construction process. The Word-Category RIG Matrix is a 103,998 by 252 matrix, where rows correspond to words of Leicester Scientific Dictionary-Core (LScDC) [2] and columns correspond to 252 Web of Science (WoS) categories [3, 4, 5]. Each entry in the matrix corresponds to a pair (category,word). Its value for the pair shows the Relative Information Gain (RIG) on the belonging of a text from the LSC to the category from observing the word in this text. The CSV file of Word-Category RIG Matrix in the published archive is presented with two additional columns of the sum of RIGs in categories and the maximum of RIGs over categories (last two columns of the matrix). So, the file ‘Word-Category RIG Matrix.csv’ contains a total of 254 columns.This matrix is created to be used in future research on quantifying of meaning in scientific texts under the assumption that words have scientifically specific meanings in subject categories and the meaning can be estimated by information gains from word to categories. LScT (Leicester Scientific Thesaurus) is a scientific thesaurus of English. The thesaurus includes a list of 5,000 words from the LScDC. We consider ordering the words of LScDC by the sum of their RIGs in categories. That is, words are arranged in their informativeness in the scientific corpus LSC. Therefore, meaningfulness of words evaluated by words’ average informativeness in the categories. We have decided to include the most informative 5,000 words in the scientific thesaurus. Words as a Vector of Frequencies in WoS CategoriesEach word of the LScDC is represented as a vector of frequencies in WoS categories. Given the collection of the LSC texts, each entry of the vector consists of the number of texts containing the word in the corresponding category.It is noteworthy that texts in a corpus do not necessarily belong to a single category, as they are likely to correspond to multidisciplinary studies, specifically in a corpus of scientific texts. In other words, categories may not be exclusive. There are 252 WoS categories and a text can be assigned to at least 1 and at most 6 categories in the LSC. Using the binary calculation of frequencies, we introduce the presence of a word in a category. We create a vector of frequencies for each word, where dimensions are categories in the corpus.The collection of vectors, with all words and categories in the entire corpus, can be shown in a table, where each entry corresponds to a pair (word,category). This table is build for the LScDC with 252 WoS categories and presented in published archive with this file. The value of each entry in the table shows how many times a word of LScDC appears in a WoS category. The occurrence of a word in a category is determined by counting the number of the LSC texts containing the word in a category. Words as a Vector of Relative Information Gains Extracted for CategoriesIn this section, we introduce our approach to representation of a word as a vector of relative information gains for categories under the assumption that meaning of a word can be quantified by their information gained for categories.For each category, a function is defined on texts that takes the value 1, if the text belongs to the category, and 0 otherwise. For each word, a function is defined on texts that takes the value 1 if the word belongs to the text, and 0 otherwise. Consider LSC as a probabilistic sample space (the space of equally probable elementary outcomes). For the Boolean random variables, the joint probability distribution, the entropy and information gains are defined.The information gain about the category from the word is the amount of information on the belonging of a text from the LSC to the category from observing the word in the text [6]. We used the Relative Information Gain (RIG) providing a normalised measure of the Information Gain. This provides the ability of comparing information gains for different categories. The calculations of entropy, Information Gains and Relative Information Gains can be found in the README file in the archive published. Given a word, we created a vector where each component of the vector corresponds to a category. Therefore, each word is represented as a vector of relative information gains. It is obvious that the dimension of vector for each word is the number of categories. The set of vectors is used to form the Word-Category RIG Matrix, in which each column corresponds to a category, each row corresponds to a word and each component is the relative information gain from the word to the category. In Word-Category RIG Matrix, a row vector represents the corresponding word as a vector of RIGs in categories. We note that in the matrix, a column vector represents RIGs of all words in an individual category. If we choose an arbitrary category, words can be ordered by their RIGs from the most informative to the least informative for the category. As well as ordering words in each category, words can be ordered by two criteria: sum and maximum of RIGs in categories. The top n words in this list can be considered as the most informative words in the scientific texts. For a given word, the sum and maximum of RIGs are calculated from the Word-Category RIG Matrix.RIGs for each word of LScDC in 252 categories are calculated and vectors of words are formed. We then form the Word-Category RIG Matrix for the LSC. For each word, the sum (S) and maximum (M) of RIGs in categories are calculated and added at the end of the matrix (last two columns of the matrix). The Word-Category RIG Matrix for the LScDC with 252 categories, the sum of RIGs in categories and the maximum of RIGs over categories can be found in the database.Leicester Scientific Thesaurus (LScT)Leicester Scientific Thesaurus (LScT) is a list of 5,000 words form the LScDC [2]. Words of LScDC are sorted in descending order by the sum (S) of RIGs in categories and the top 5,000 words are selected to be included in the LScT. We consider these 5,000 words as the most meaningful words in the scientific corpus. In other words, meaningfulness of words evaluated by words’ average informativeness in the categories and the list of these words are considered as a ‘thesaurus’ for science. The LScT with value of sum can be found as CSV file with the published archive. Published archive contains following files:1) Word_Category_RIG_Matrix.csv: A 103,998 by 254 matrix where columns are 252 WoS categories, the sum (S) and the maximum (M) of RIGs in categories (last two columns of the matrix), and rows are words of LScDC. Each entry in the first 252 columns is RIG from the word to the category. Words are ordered as in the LScDC.2) Word_Category_Frequency_Matrix.csv: A 103,998 by 252 matrix where columns are 252 WoS categories and rows are words of LScDC. Each entry of the matrix is the number of texts containing the word in the corresponding category. Words are ordered as in the LScDC.3) LScT.csv: List of words of LScT with sum (S) values. 4) Text_No_in_Cat.csv: The number of texts in categories. 5) Categories_in_Documents.csv: List of WoS categories for each document of the LSC.6) README.txt: Description of Word-Category RIG Matrix, Word-Category Frequency Matrix and LScT and forming procedures.7) README.pdf (same as 6 in PDF format)References[1] Suzen, Neslihan (2019): LSC (Leicester Scientific Corpus). figshare. Dataset. https://doi.org/10.25392/leicester.data.9449639.v2[2] Suzen, Neslihan (2019): LScDC (Leicester Scientific Dictionary-Core). figshare. Dataset. https://doi.org/10.25392/leicester.data.9896579.v3[3] Web of Science. (15 July). Available: https://apps.webofknowledge.com/[4] WoS Subject Categories. Available: https://images.webofknowledge.com/WOKRS56B5/help/WOS/hp_subject_category_terms_tasca.html [5] Suzen, N., Mirkes, E. M., & Gorban, A. N. (2019). LScDC-new large scientific dictionary. arXiv preprint arXiv:1912.06858. [6] Shannon, C. E. (1948). A mathematical theory of communication. Bell system technical journal, 27(3), 379-423.

The Core Based Statistical Areas dataset was updated on September 22, 2025 from the U.S. Department of Commerce, U.S. Census Bureau, Geography Division and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). This resource is a member of a series. 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) System (MTS). The MTS 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. Metropolitan and Micropolitan Statistical Areas are together termed Core Based Statistical Areas (CBSAs) and are defined by the Office of Management and Budget (OMB) and consist of the county or counties or equivalent entities associated with at least one urban core of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core. Categories of CBSAs are: Metropolitan Statistical Areas, based on urban areas of 50,000 or more population; and Micropolitan Statistical Areas, based on urban areas of at least 10,000 population but less than 50,000 population. The CBSA boundaries are those defined by OMB based on the 2020 Census and published in 2023. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529014

The set contains statistical information, including references to resources

The primary objective from this project was to acquire historical shoreline information for all of the Northern Ireland coastline. Having this detailed understanding of the coast’s shoreline position and geometry over annual to decadal time periods is essential in any management of the coast.The historical shoreline analysis was based on all available Ordnance Survey maps and aerial imagery information. Analysis looked at position and geometry over annual to decadal time periods, providing a dynamic picture of how the coastline has changed since the start of the early 1800s.Once all datasets were collated, data was interrogated using the ArcGIS package – Digital Shoreline Analysis System (DSAS). DSAS is a software package which enables a user to calculate rate-of-change statistics from multiple historical shoreline positions. Rate-of-change was collected at 25m intervals and displayed both statistically and spatially allowing for areas of retreat/accretion to be identified at any given stretch of coastline.The DSAS software will produce the following rate-of-change statistics:Net Shoreline Movement (NSM) – the distance between the oldest and the youngest shorelines.Shoreline Change Envelope (SCE) – a measure of the total change in shoreline movement considering all available shoreline positions and reporting their distances, without reference to their specific dates.End Point Rate (EPR) – derived by dividing the distance of shoreline movement by the time elapsed between the oldest and the youngest shoreline positions.Linear Regression Rate (LRR) – determines a rate of change statistic by fitting a least square regression to all shorelines at specific transects.Weighted Linear Regression Rate (WLR) - calculates a weighted linear regression of shoreline change on each transect. It considers the shoreline uncertainty giving more emphasis on shorelines with a smaller error.The end product provided by Ulster University is an invaluable tool and digital asset that has helped to visualise shoreline change and assess approximate rates of historical change at any given coastal stretch on the Northern Ireland coast.

Context

A team's mean seasons statistics can be used as predictors for their performance in future games. However, these statistics gain additional meaning when placed in the context of their opponents' (and opponents' opponents') performance. This dataset provides this context for each team. Furthermore, predicting games based on post-season stats causes data leakage, which from experience can be significant in this context (15-20% loss in accuracy). Thus, this dataset provides each of these statistics prior to each game of the regular season, preventing any source of data leakage.

Content

All data is derived from the March Madness competition data. Each original column was renamed to "A" and "B" instead of "W" and "L," and the mirrored to represent both orderings of opponents. Each team's mean stats are computed (both their stats, and the mean "allowed" or "forced" statistics by their opponents). To compute the mean opponents' stats, we analyze the games played by each opponent (excluding games played against the team in question), and compute the mean statistics for those games. We then compute the mean of these mean statistics, weighted by the number of times the team in question played each opponent. The opponents' opponent's stats are computed as a weighted average of the opponents' average. This results in statistics similar to those used to compute strength of schedule or RPI, just that they go beyond win percentages (See: https://en.wikipedia.org/wiki/Rating_percentage_index)

The per game statistics are computed by pretending we don't have any of the data on or after the day in question.

Next Steps

Currently, the data isn't computed particularly efficiently. Computing the per game averages for every day of the season is necessary to compute fully accurate opponents' opponents' average, but takes about 90 minutes to obtain. It is probably possible to parallelize this, and the per-game averages involve a lot of repeated computation (basically computing the final averages over and over again for each day). Speeding this up will make it more convenient to make changes to the dataset.

I would like to transform these statistics to be per-possession, add shooting percentages, pace, and number of games played (to give an idea of the amount uncertainty that exists in the per-game averages). Some of these can be approximated with the given data (but the results won't be exact), while others will need to be computed from scratch.

Values in parenthesis are for the highest resolution shell.aRsym = Σhkl Σi | Ii(hkl)- 〈I(hkl)〉| / Σhkl ΣiIi(hkl), where Ii(hkl) is the intensity of an individual measurement of the symmetry related reflection and 〈I(hkl)〉 is the mean intensity of the symmetry related reflections.bI/σ is defined as the ratio of averaged value of the intensity to its standard deviation.cRcryst = Σhkl ||Fobs|—|Fcalc||/ Σhkl |Fobs|, where Fobs and Fcalc are the observed and calculated structure-factor amplitudes.dRfree was calculated as Rcryst using randomly selected 5% of the unique reflections that were omitted from the structure refinement.eRamachandran statistics indicate the percentage of residues in the most favored, additionally allowed and outlier regions of the Ramachandran diagram as defined by MOLPROBITY.Statistics for data collection and refinement.

The set of data contains statistical information on the work with the appeals of citizens of the State Service for Quality of Education of Ukraine. In particular, where the appeals came from, the number of appeals, the number of citizens who applied.

The set contains administrative data within the meaning of the Law of Ukraine "On State Statistics", which are collected (processed) and are subject to disclosure in accordance with the requirements of the law by the information manager (statistical report 85-K)

Costa Rica CR: Population Density: People per Square Km data was reported at 100.939 Person/sq km in 2021. This records an increase from the previous number of 100.335 Person/sq km for 2020. Costa Rica CR: Population Density: People per Square Km data is updated yearly, averaging 63.443 Person/sq km from Dec 1961 (Median) to 2021, with 61 observations. The data reached an all-time high of 100.939 Person/sq km in 2021 and a record low of 27.343 Person/sq km in 1961. Costa Rica CR: Population Density: People per Square Km data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Costa Rica – Table CR.World Bank.WDI: Population and Urbanization Statistics. Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.;Food and Agriculture Organization and World Bank population estimates.;Weighted average;

Abstract The research aimed to identify the differentiation of meanings of work among beauty salon workers, considering the work contracts and the functions performed (hairdressers and manicurists), in a context of pejotização and functions’ internal hierarchy. We applied questionnaires to 171 manicurists and hairdressers with the following types of links: employee, informal, MEI pejotizado and MEI não pejotizado. The results indicated that employees perceive with greater intensity the work as a responsibility and as a way of being socially included, and more proportionality in social and financial retribution. They also indicated that manicurists experience with more intensity the characteristics of brutalization, discrimination and demand.

The set contains a list of norms of the state statistical observation of the executive committee of Bila Tserkva City Council

A set of data includes resources containing statistical data included in the forms of 8-n(year) "Report on the activities of the museum", 7-NC "Report on the activities of club establishments", 7-NC "Consolidated report on the activities of club establishments", 6-nk "Report on the activities of state, public libraries, centralized library systems (CBS), which are classified in the sphere of management of the Ministry of Culture and Tourism of Ukraine", 80-a-RVC "Consolidated Reporting, State, Public and Other Libraries", 1-MSh (annual summary) "Consolidated report of art schools, specialized art schools (boarding schools) of the Ministry of Culture of Ukraine", as well as statistical forms K-2-RVK(2) "Consolidated report on the presence and activities of film demonstrators", 1-PKC (annual) "Report on immovable monuments and objects of cultural heritage (PKC)", 4-f "Report on precious metals and precious stones contained in museum objects"

The primary objective from this project was to acquire historical shoreline information for all of the Northern Ireland coastline. Having this detailed understanding of the coast’s shoreline position and geometry over annual to decadal time periods is essential in any management of the coast.The historical shoreline analysis was based on all available Ordnance Survey maps and aerial imagery information. Analysis looked at position and geometry over annual to decadal time periods, providing a dynamic picture of how the coastline has changed since the start of the early 1800s.Once all datasets were collated, data was interrogated using the ArcGIS package – Digital Shoreline Analysis System (DSAS). DSAS is a software package which enables a user to calculate rate-of-change statistics from multiple historical shoreline positions. Rate-of-change was collected at 25m intervals and displayed both statistically and spatially allowing for areas of retreat/accretion to be identified at any given stretch of coastline.The DSAS software will produce the following rate-of-change statistics:Net Shoreline Movement (NSM) – the distance between the oldest and the youngest shorelines.Shoreline Change Envelope (SCE) – a measure of the total change in shoreline movement considering all available shoreline positions and reporting their distances, without reference to their specific dates.End Point Rate (EPR) – derived by dividing the distance of shoreline movement by the time elapsed between the oldest and the youngest shoreline positions.Linear Regression Rate (LRR) – determines a rate of change statistic by fitting a least square regression to all shorelines at specific transects.Weighted Linear Regression Rate (WLR) - calculates a weighted linear regression of shoreline change on each transect. It considers the shoreline uncertainty giving more emphasis on shorelines with a smaller error.The end product provided by Ulster University is an invaluable tool and digital asset that has helped to visualise shoreline change and assess approximate rates of historical change at any given coastal stretch on the Northern Ireland coast.

The tables presents indices (2005=100) and changes on twelve months previously (%) of production, turnover and orders in industry (excl. construction), by sector of industry.

Data available : January 2000 till December 2012

Table has been discontinued as from 22 March 2013 due to change of the base year from 2005 to 2010. Statistics Netherlands has started a new table, Industry; production, sales and orders, changes and index (2010 = 100). For more information see sections 3 and 4.

Status of the figures: Production: three most recent months: provisional. The figures within a reporting year are revised provisional figures until publication in December of the year concerned. Turnover: three most recent months: provisional. Orders: three most recent months: provisional.

Changes as of 8 July 2011. Due to new regulations (European System for National Accounts, 2010, Balance of Payments Manual 6) for National Accounts and Balance of Payment, the turnover definition has been adapted. This results in adjustments in production index and other short term statistics. The adaptation of the turnover definition is related to a change in registration of enterprises that (partially) contract out their production abroad. The adjustment means that goods dealt with by foreign subsidiaries of Dutch parent companies do count for Dutch production. Goods dealt with in the Netherlands by Dutch subsidiaries of foreign parent companies that remain property of these parent companies do no longer count as Dutch production. However, they count as export of services for the sum that has been added to value in the Netherlands. Until December 2009, index figures for manufacturing turnover are based on the previous turnover definition. From January 2010 onwards, the turnover figures are based on the new turnover definition. Therefore, turnover changes 2010 on 2009 are not accurate.

This dataset is the definitive set of annually released statistical area 1 (SA1) boundaries for 2022 as defined by Stats NZ (the custodian). This version contains 29,913 SA1 features.

SA1s were introduced as part of the Statistical Standard for Geographic Areas 2018 (SSGA18) which replaced the New Zealand Standard Areas Classification (NZSAC92). SA1 is an output geography that allows the release of more detailed information about population characteristics than is available at the meshblock level.

Built by joining meshblocks, SA1s have an ideal size range of 100–200 residents, and a maximum population of about 500. This is to minimise suppression of population data in multivariate statistics tables. SA1s either define or aggregate to define SA2s, urban rural areas, territorial authorities, and regional councils. Some SA1s that contain apartment blocks, retirement villages, and large non-residential facilities have more than 500 residents.

This generalised version has been simplified for rapid drawing and is designed for thematic or web mapping purposes.

Digital boundary data became freely available on 1 July 2007.

The SA1 classification can also be downloaded from the Stats NZ classification and concordance tool Ariā.