A harmonized collection of the core data pertaining to COVID-19 reported cases by geography, in a format prepared for analysis

This SOils DAta Harmonization (SoDaH) database is designed to bring together soil carbon data from diverse research networks into a harmonized dataset that can be used for synthesis activities and model development. The research network sources for SoDaH span different biomes and climates, encompass multiple ecosystem types, and have collected data across a range of spatial, temporal, and depth gradients. The rich data sets assembled in SoDaH consist of observations from monitoring efforts and long-term ecological experiments. The SoDaH database also incorporates related environmental covariate data pertaining to climate, vegetation, soil chemistry, and soil physical properties. The data are harmonized and aggregated using open-source code that enables a scripted, repeatable approach for soil data synthesis.

Supplementary Material 3.

Abstract copyright UK Data Service and data collection copyright owner.

The Cohort and Longitudinal Studies Enhancement Resources (CLOSER) project aims to maximise the use, value and impact of longitudinal research. It brings together leading longitudinal studies, the British Library and the UK Data Service, to stimulate interdisciplinary research, develop shared resources, provide training, and share expertise. Resources available from CLOSER include harmonised datasets (developed to facilitate cross-study comparisons) and CLOSER Discovery (a search engine that enables researchers to search and browse questionnaires and data from different longitudinal studies). CLOSER’s website also includes a range of training materials focused on longitudinal studies and data. CLOSER is funded by the Economic and Social Research Council (ESRC) and is based at the UCL Institute of Education.

The CLOSER Work Package 2 provides the harmonisation of socio-economic measures between the National Survey of Health and Development (NSHD), National Child Development Study (NCDS), 1970 British Cohort Study (BCS70), and the Millennium Cohort Study (MCS).

Further information can be found on the CLOSER website. The content of longitudinal studies, including questions, topics and variables can be explored via the CLOSER Discovery website.

The Harmonised Socio-Economic Measures in Four Longitudinal Cohort Studies: MRC National Survey of Health and Development: Special Licence Access provides the harmonised socio-economic measures for the NSHD.

For the second edition (October 2019), revised data and documentation have been deposited.

In June 2022, all cases for two respondents were set to missing at the depositor's request.

Main Topics:
The main topics covered are:

• socio-economic measures;
• occupational classification;
  
• social and occupational mobility.
  

Abstract copyright UK Data Service and data collection copyright owner.

The Cohort and Longitudinal Studies Enhancement Resources (CLOSER) project aims to maximise the use, value and impact of longitudinal research. It brings together leading longitudinal studies, the British Library and the UK Data Service, to stimulate interdisciplinary research, develop shared resources, provide training, and share expertise. Resources available from CLOSER include harmonised datasets (developed to facilitate cross-study comparisons) and CLOSER Discovery (a search engine that enables researchers to search and browse questionnaires and data from different longitudinal studies).

The CLOSER Work Package 9 provides data harmonisation of childhood environment and adult wellbeing measures between the National Survey of Health and Development (NSHD), National Child Development Study (NCDS), and 1970 British Cohort Study (BCS70). The CLOSER website also includes a range of training materials focused on longitudinal studies and data. CLOSER is funded by the Economic and Social Research Council (ESRC) and is based at the UCL Institute of Education.

The Harmonised Childhood Environment and Adult Wellbeing Measures in Three Longitudinal Cohort Studies: 1970 British Cohort Study provides the harmonisation of childhood environment and adult wellbeing measures for BCS70.

Main Topics:
The main topics covered are:

• Childhood environment;
• Mental wellbeing;
• Parent-child relationships;
• Health;
• Parenting;
• Child rearing;
• Housing;
• Family.

This table includes figures on the effects of rent harmonisation and renovation on the average rent increase. A distinction is made here between rental of dwellings by social and other landlords and liberalised rental.

Data available from: 2015.

Status of the figures: The figures in this table are definitive.

Changes as of 4 September 2024: The figures of 2024 have been published.

When will new figures be published? New figures will become available in September 2025.

The LUCAS LUC future dataset consists of annual land use and land cover maps from 2016 to 2100. It is based on land cover data from the LANDMATE PFT dataset for the year 2015. The LANDMATE PFT consists of 16 plant functional types and non-vegetated classes that were converted from the ESA-CCI LC land cover data according to the method of Reinhart et al. (2021). The land use change information from the Land-Use Harmonization Data Set version 2 (LUH2 v2.1f, Hurtt et al. 2020) were imposed using the land use translator developed by Hoffmann et al. (2021). The projected land use change information was derived for different Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) combinations used in the framework of the 6th phase of Coupled Modelling Intercomparison Project (CMIP6). For each year, a map is provided that contains 16 fields. Each field holds the fraction the respective plant functional types and non-vegetated classes in the total grid cell (0-1). The LUCAS LUC dataset was constructed within the HICSS project LANDMATE and the WCRP flagship pilot study LUCAS to meet the requirements of downscaling experiments within EURO-CORDEX. Plant functional types and non-vegetated classes: 1 - Tropical broadleaf evergreen trees 2 - Tropical deciduous trees 3 - Temperate broadleaf evergreen trees 4 - Temperate deciduous trees 5 - Evergreen coniferous trees 6 - Deciduous coniferous trees 7 - Coniferous shrubs 8 - Deciduous shrubs 9 - C3 grass 10 - C4 grass 11 - Tundra 12 - Swamp 13 - Non-irrigated crops 14 - Irrigated crops 15 - Urban 16 - Bare

The datasets in the .pdf and .zip attached to this record are in support of Intelligent Transportation Systems Joint Program Office (ITS JPO) report FHWA-JPO-15-222, "Impacts Assessment of Dynamic Speed Harmonization with Queue Warning: Task 3, Impacts Assessment Report". The files in these zip files are specifically related to the US-101 Testbed, near San Mateo, CA. The uncompressed and compressed files total 2.0265 GB in size. The files have been uploaded as-is; no further documentation was supplied by NTL. All located .docx files were converted to .pdf document files which are an open, archival format. These .pdfs were then added to the zip file alongside the original .docx files. The attached zip files can be unzipped using any zip compression/decompression software. These zip file contains files in the following formats: .pdf document files which can be read using any pdf reader; .xlsxm macro-enabled spreadsheet files which can be read in Microsoft Excel and some Tech Report spreadsheet programs; .accdb database files which may be opened with Microsoft Access Database software and Tech Report open database software applications ; as well as .db generic database files, often associated with thumbnail images in the Windows operating environment. [software requirements] These files were last accessed in 2017. File and .zip file names include: FHWA_JPO_15_222_INFLO_Performance_Measure_METADATA.pdf ; FHWA_JPO_15_222_INFLO_Performance_Measure_METADATA.docx ; FHWA_JPO_15_222_INFLO_VISSIM_Output_and_Analysis_Spreadsheets.zip ; FHWA_JPO_15_222_INFLO_Spreadsheet_PDFs.zip ; FHWA_JPO_15_222_DATA_CV50.zip ; and, FHWA_JPO_15_222_DATA_CV25.zip

The Fundamental Data Record (FDR) for Atmospheric Composition UVN v.1.0 dataset is a cross-instrument Level-1 product [ATMOS_L1B] generated in 2023 and resulting from the ESA FDR4ATMOS project. The FDR contains selected Earth Observation Level 1b parameters (irradiance/reflectance) from the nadir-looking measurements of the ERS-2 GOME and Envisat SCIAMACHY missions for the period ranging from 1995 to 2012. The data record offers harmonised cross-calibrated spectra with focus on spectral windows in the Ultraviolet-Visible-Near Infrared regions for the retrieval of critical atmospheric constituents like ozone (O3), sulphur dioxide (SO2), nitrogen dioxide (NO2) column densities, alongside cloud parameters. The FDR4ATMOS products should be regarded as experimental due to the innovative approach and the current use of a limited-sized test dataset to investigate the impact of harmonization on the Level 2 target species, specifically SO2, O3 and NO2. Presently, this analysis is being carried out within follow-on activities. The FDR V1 is currently being extended to include the MetOp GOME-2 series. Product format For many aspects, the FDR product has improved compared to the existing individual mission datasets: GOME solar irradiances are harmonised using a validated SCIAMACHY solar reference spectrum, solving the problem of the fast-changing etalon present in the original GOME Level 1b data; Reflectances for both GOME and SCIAMACHY are provided in the FDR product. GOME reflectances are harmonised to degradation-corrected SCIAMACHY values, using collocated data from the CEOS PIC sites; SCIAMACHY data are scaled to the lowest integration time within the spectral band using high-frequency PMD measurements from the same wavelength range. This simplifies the use of the SCIAMACHY spectra which were split in a complex cluster structure (with own integration time) in the original Level 1b data; The harmonization process applied mitigates the viewing angle dependency observed in the UV spectral region for GOME data; Uncertainties are provided. Each FDR product provides, within the same file, irradiance/reflectance data for UV-VIS-NIR special regions across all orbits on a single day, including therein information from the individual ERS-2 GOME and Envisat SCIAMACHY measurements. FDR has been generated in two formats: Level 1A and Level 1B targeting expert users and nominal applications respectively. The Level 1A [ATMOS_L1A] data include additional parameters such as harmonisation factors, PMD, and polarisation data extracted from the original mission Level 1 products. The ATMOS_L1A dataset is not part of the nominal dissemination to users. In case of specific requirements, please contact EOHelp. Please refer to the README file for essential guidance before using the data. All the new products are conveniently formatted in NetCDF. Free standard tools, such as Panoply, can be used to read NetCDF data. Panoply is sourced and updated by external entities. For further details, please consult our Terms and Conditions page. Uncertainty characterisation One of the main aspects of the project was the characterization of Level 1 uncertainties for both instruments, based on metrological best practices. The following documents are provided: General guidance on a metrological approach to Fundamental Data Records (FDR) Uncertainty Characterisation document Effect tables NetCDF files containing example uncertainty propagation analysis and spectral error correlation matrices for SCIAMACHY (Atlantic and Mauretania scene for 2003 and 2010) and GOME (Atlantic scene for 2003) reflectance_uncertainty_example_FDR4ATMOS_GOME.nc reflectance_uncertainty_example_FDR4ATMOS_SCIA.nc

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE DEPARTMENT OF STATISTICS OF THE HASHEMITE KINGDOM OF JORDAN

The Department of Statistics (DOS) carried out four rounds of the 2011 Employment and Unemployment Survey (EUS). The survey rounds covered a total sample of about 52544 thousand households Nation-wide.The sampled households were selected using a stratified multi-stage cluster sampling design. It is noteworthy that the sample represents the national level (Kingdom), governorates, the three Regions (Central, North and South), and the urban/rural areas.

The importance of this survey lies in that it provides a comprehensive data base on employment and unemployment that serves decision makers, researchers as well as other parties concerned with policies related to the organization of the Jordanian labor market.

It is worthy to mention that the DOS employed new technology in data collection and data processing. Data was collected using electronic questionnaire instead of a hard copy, namely a hand held device (PDA).

The survey main objectives are:

• To identify the demographic, social and economic characteristics of the population and manpower.
• To identify the occupational structure and economic activity of the employed persons, as well as their employment status.
• To identify the reasons behind the desire of the employed persons to search for a new or additional job.
• To measure the economic activity participation rates (the number of economically active population divided by the population of 15+ years old).
• To identify the different characteristics of the unemployed persons.
• To measure unemployment rates (the number of unemployed persons divided by the number of economically active population of 15+ years old) according to the various characteristics of the unemployed, and the changes that might take place in this regard.
• To identify the most important ways and means used by the unemployed persons to get a job, in addition to measuring durations of unemployment for such persons.
• To identify the changes overtime that might take place regarding the above-mentioned variables.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing labor force surveys in several Arab countries.

Geographic coverage

Covering a sample representative on the national level (Kingdom), governorates, the three Regions (Central, North and South), and the urban/rural areas.

Analysis unit

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

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE DEPARTMENT OF STATISTICS OF THE HASHEMITE KINGDOM OF JORDAN

Survey Frame

The sample of this survey is based on the frame provided by the data of the Population and Housing Census, 2004. The Kingdom was divided into strata, where each city with a population of 100,000 persons or more was considered as a large city. The total number of these cities is 6. Each governorate (except for the 6 large cities) was divided into rural and urban areas. The rest of the urban areas in each governorate was considered as an independent stratum. The same was applied to rural areas where it was considered as an independent stratum. The total number of strata was 30.

In view of the existing significant variation in the socio-economic characteristics in large cities in particular and in urban in general, each stratum of the large cities and urban strata was divided into four sub-stratum according to the socio- economic characteristics provided by the population and housing census with the purpose of providing homogeneous strata.

The frame excludes the population living in remote areas (most of whom are nomads), In addition to that, the frame does not include collective dwellings, such as hotels, hospitals, work camps, prisons and alike.

Sample Design

The sample of this survey was designed using the cluster stratified sampling method. It is representative at the Kingdom, rural and urban areas, regions and governorates levels. The Primary Sampling Units (clusters) were distributed to governorates, urban and rural areas and large cities in each governorate according to the weight of persons/households and according to the variance within each stratum. Slight modifications regarding the number of these units were made. The Primary Sampling Units (PSUs) were ordered within each stratum according to geographic characteristics and then according to socio-economic characteristics in order to ensure good spread of the sample. Then, the sample were selected on two stages. In the first stage, the PSUs were selected using the Probability Proportionate to Size with systematic selection procedure. The number of households, in each PSU served as its weight or size. In the second stage, the blocks of the PSUs which were selected in the first stage have been updated. Then a constant number of households was selected, using the random systematic sampling method as final PSUs from each PSU (cluster).

Sampling notes

It is noteworthy that the sample of the present survey does not represent the non-Jordanian population, due to the fact that it is based on households living in conventional dwellings. In other words, it does not cover the collective households living in collective dwellings. Therefore, the non-Jordanian households covered in the present survey are either private households or collective households living in conventional dwellings. In Jordan, it is well known that a large number of non-Jordanian workers live as groups and spend most of their time at workplaces. Hence, it is more unlikely to find them at their residences during daytime (i.e. the time when the data of the survey is collected). Furthermore, most of them live in their workplaces, such as: workshops, sales stores, guard places, or under construction building's sites. Such places are not classified as occupied dwellings for household sampling purposes. Due to all of the above, the coverage of such population would not be complete in household surveys.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was designed electronically on the PDA and revised by the DOS technical staff. It was finalized upon completion of the training program. The questionnaire is divided into main topics, each containing a clear and consistent group of questions, and designed in a way that facilitates the electronic data entry and verification. The questionnaire includes the characteristics of household members in addition to the identification information, which reflects the administrative as well as the statistical divisions of the Kingdom.

Cleaning operations

Raw Data

PDAs were used to input and transfer data from the interviewees to the database. The plan of the tabulation of survey results was guided by former Employment and Unemployment Surveys which were previously prepared and tested. When all data processing procedures were completed, the actual survey results were tabulated using an ORACLE package. The tabulations were then thoroughly checked for consistency of data such as titles, inputs, concepts, as well as the figures.

Harmonized Data

• STATA package is used to clean and harmonize the datasets.
• The harmonization process starts with a cleaning process for all raw data files received from the Statistical Agency.
• All cleaned data files are then merged to produce one data file on the individual level containing all variables subject to harmonization.
• A country-specific program is generated for each dataset to generate/ compute/ recode/ rename/ format/ label harmonized variables.
• A post-harmonization cleaning process is then conducted on the data.
• Harmonized data is saved on the household as well as the individual level, in STATA and then converted to SPSS, to be disseminated.

Contains supplementary marker gene information. (XLS 117 kb)

Multicenter and multi-scanner imaging studies may be necessary to ensure sufficiently large sample sizes for developing accurate predictive models. However, multicenter studies, incorporating varying research participant characteristics, MRI scanners, and imaging acquisition protocols, may introduce confounding factors, potentially hindering the creation of generalizable machine learning models. Models developed using one dataset may not readily apply to another, emphasizing the importance of classification model generalizability in multi-scanner and multicenter studies for producing reproducible results. This study focuses on enhancing generalizability in classifying individual migraine patients and healthy controls using brain MRI data through a data harmonization strategy. We propose identifying a ’healthy core’—a group of homogeneous healthy controls with similar characteristics—from multicenter studies. The Maximum Mean Discrepancy (MMD) in Geodesic Flow Kernel (GFK) space is employed to compare two datasets, capturing data variabilities and facilitating the identification of this ‘healthy core’. Homogeneous healthy controls play a vital role in mitigating unwanted heterogeneity, enabling the development of highly accurate classification models with improved performance on new datasets. Extensive experimental results underscore the benefits of leveraging a ’healthy core’. We utilized two datasets: one comprising 120 individuals (66 with migraine and 54 healthy controls), and another comprising 76 individuals (34 with migraine and 42 healthy controls). Notably, a homogeneous dataset derived from a cohort of healthy controls yielded a significant 25% accuracy improvement for both episodic and chronic migraineurs.

The use of multi-site datasets in neuroimaging provides neuroscientists with more statistical power to perform their analyses. However, it has been shown that the imaging-site introduces variability in the data that cannot be attributed to biological sources. In this work, we show that functional connectivity matrices derived from resting-state multi-site data contain a significant imaging-site bias. To this aim, we exploited the fact that functional connectivity matrices belong to the manifold of symmetric positive-definite (SPD) matrices, making it possible to operate on them with Riemannian geometry. We hereby propose a geometry-aware harmonization approach, Rigid Log-Euclidean Translation, that accounts for this site bias. Moreover, we adapted other Riemannian-geometric methods designed for other domain adaptation tasks and compared them to our proposal. Based on our results, Rigid Log-Euclidean Translation of multi-site functional connectivity matrices seems to be among the studied methods the most suitable in a clinical setting. This represents an advance with respect to previous functional connectivity data harmonization approaches, which do not respect the geometric constraints imposed by the underlying structure of the manifold. In particular, when applying our proposed method to data from the ADHD-200 dataset, a multi-site dataset built for the study of attention-deficit/hyperactivity disorder, we obtained results that display a remarkable correlation with established pathophysiological findings and, therefore, represent a substantial improvement when compared to the non-harmonization analysis. Thus, we present evidence supporting that harmonization should be extended to other functional neuroimaging datasets and provide a simple geometric method to address it.

Replication datasets for the retroharmonize Case Study: Working With Arab Barometer Surveys

Abstract

This multi-country harmonized dataset concerning forcibly displaced populations (FDPs) and their host communities was produced by the World Bank’s Poverty and Equity Global Practice. It incorporates representative surveys conducted in 10 countries across five regions that hosted FDPs in the period 2015 to 2020. The goal of this harmonization exercise is to provide researchers and policymakers with a valuable input for comparative analyses of forced displacement across key developing country settings.

Geographic coverage

The datasets included in the harmonization effort cover key recent displacement contexts: the Venezuelan influx in Latin America’s Andean states; the Syrian crisis in the Mashreq; the Rohingya displacement in Bangladesh; and forcible displacement in Sub-Saharan Africa (Sahel and East Africa). The harmonization exercise encompasses 10 different surveys. These include nationally representative surveys with a separate representative stratum for displaced populations; sub-national representative surveys covering displaced populations and their host communities; and surveys designed specifically to provide insights on displacement contexts. Most of the surveys were collected between 2015 and 2020.

Analysis unit

Household

Universe

Forcibly displaced populations and their hosts communities.

Kind of data

Sample survey data [ssd]

Mode of data collection

Computer Assisted Personal Interview [capi]

Trait data represent the basis for ecological and evolutionary research and have relevance for biodiversity conservation, ecosystem management and earth system modelling. The collection and mobilization of trait data has strongly increased over the last decade, but many trait databases still provide only species-level, aggregated trait values (e.g. ranges, means) and lack the direct observations on which those data are based. Thus, the vast majority of trait data measured directly from individuals remains hidden and highly heterogeneous, impeding their discoverability, semantic interoperability, digital accessibility and (re-)use. Here, we integrate quantitative measurements of verbatim trait information from plant individuals (e.g. lengths, widths, counts and angles of stems, leaves, fruits and inflorescence parts) from multiple sources such as field observations and herbarium collections. We develop a workflow to harmonize heterogeneous trait measurements (e.g. trait names and their values and units) as well as additional information related to taxonomy, measurement or fact and occurrence. This data integration and harmonization builds on vocabularies and terminology from existing metadata standards and ontologies such as the Ecological Trait-data Standard (ETS), the Darwin Core (DwC), the Thesaurus Of Plant characteristics (TOP) and the Plant Trait Ontology (TO). A metadata form filled out by data providers enables the automated integration of trait information from heterogeneous datasets. We illustrate our tools with data from palms (family Arecaceae), a globally distributed (pantropical), diverse plant family that is considered a good model system for understanding the ecology and evolution of tropical rainforests. We mobilize nearly 140,000 individual palm trait measurements in an interoperable format, identify semantic gaps in existing plant trait terminology and provide suggestions for the future development of a thesaurus of plant characteristics. Our work thereby promotes the semantic integration of plant trait data in a machine-readable way and shows how large amounts of small trait data sets and their metadata can be integrated into standardized data products.

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE PALESTINIAN CENTRAL BUREAU OF STATISTICS

The Palestinian Central Bureau of Statistics (PCBS) carried out four rounds of the Labor Force Survey 2012 (LFS). The survey rounds covered a total sample of about 30,887 households, and the number of completed questionaire is 26,898.

The main objective of collecting data on the labour force and its components, including employment, unemployment and underemployment, is to provide basic information on the size and structure of the Palestinian labour force. Data collected at different points in time provide a basis for monitoring current trends and changes in the labour market and in the employment situation. These data, supported with information on other aspects of the economy, provide a basis for the evaluation and analysis of macro-economic policies.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing labor force surveys in several Arab countries.

Geographic coverage

Covering a representative sample on the region level (West Bank, Gaza Strip), the locality type (urban, rural, camp) and the governorates.

Analysis unit

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

Universe

The survey covered all Palestinian households who are a usual residence of the Palestinian Territory.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE PALESTINIAN CENTRAL BUREAU OF STATISTICS

The methodology was designed according to the context of the survey, international standards, data processing requirements and comparability of outputs with other related surveys.

---> Target Population: It consists of all individuals aged 10 years and older normally residing in their households in Palestine during 2012.

---> Sampling Frame: The sampling frame consists of the master sample, which was updated in 2011: each enumeration area consists of buildings and housing units with an average of about 124 households. The master sample consists of 596 enumeration areas; we used 498 enumeration areas as a framework for the labor force survey sample in 2012 and these units were used as primary sampling units (PSUs).

---> Sampling Size: The estimated sample size in the first quarter was 7,775 households, in the second quarter it was 7,713 households, in the third quarter it was 7,695 households and in the fourth quarter it was 7,704 households.

---> Sample Design The sample is two stage stratified cluster sample with two stages : First stage: we select a systematic random sample of 498 enumeration areas for the whole round ,and we excluded the enumeration areas which its sizes less than 40 households. Second stage: we select a systematic random sample of 16 households from each enumeration area selected in the first stage, se we select a systematic random of 16 households of the enumeration areas which its size is 80 household and over and the enumeration areas which its size is less than 80 households we select systematic random of 8 households.

---> Sample strata: The population was divided by: 1- Governorate (16 governorate) 2- Type of Locality (urban, rural, refugee camps).

---> Sample Rotation: Each round of the Labor Force Survey covers all of the 498 master sample enumeration areas. Basically, the areas remain fixed over time, but households in 50% of the EAs were replaced in each round. The same households remain in the sample for two consecutive rounds, left for the next two rounds, then selected for the sample for another two consecutive rounds before being dropped from the sample. An overlap of 50% is then achieved between both consecutive rounds and between consecutive years (making the sample efficient for monitoring purposes).

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey questionnaire was designed according to the International Labour Organization (ILO) recommendations. The questionnaire includes four main parts:

---> 1. Identification Data: The main objective for this part is to record the necessary information to identify the household, such as, cluster code, sector, type of locality, cell, housing number and the cell code.

---> 2. Quality Control: This part involves groups of controlling standards to monitor the field and office operation, to keep in order the sequence of questionnaire stages (data collection, field and office coding, data entry, editing after entry and store the data.

---> 3. Household Roster: This part involves demographic characteristics about the household, like number of persons in the household, date of birth, sex, educational level…etc.

---> 4. Employment Part: This part involves the major research indicators, where one questionnaire had been answered by every 15 years and over household member, to be able to explore their labour force status and recognize their major characteristics toward employment status, economic activity, occupation, place of work, and other employment indicators.

Cleaning operations

---> Raw Data The data processing stage consisted of the following operations: 1. Editing and coding before data entry: All questionnaires were edited and coded in the office using the same instructions adopted for editing in the field. 2. Data entry: At this stage, data was entered into the computer using a data entry template designed in Access. The data entry program was prepared to satisfy a number of requirements such as: - Duplication of the questionnaires on the computer screen. - Logical and consistency check of data entered. - Possibility for internal editing of question answers. - Maintaining a minimum of digital data entry and fieldwork errors. - User friendly handling. Possibility of transferring data into another format to be used and analyzed using other statistical analytic systems such as SPSS.

---> Harmonized Data - The SPSS package is used to clean and harmonize the datasets. - The harmonization process starts with a cleaning process for all raw data files received from the Statistical Agency. - All cleaned data files are then merged to produce one data file on the individual level containing all variables subject to harmonization. - A country-specific program is generated for each dataset to generate/ compute/ recode/ rename/ format/ label harmonized variables. - A post-harmonization cleaning process is then conducted on the data. - Harmonized data is saved on the household as well as the individual level, in SPSS and then converted to STATA, to be disseminated.

Response rate

The survey sample consists of 30,887 households, of which 26,898 households completed the interview: 17,594 households from the West Bank and 9,304 households in Gaza Strip. Weights were modified to account for the non-response rate. The response rate in the West Bank was 90.2 %, while in the Gaza Strip it was 94.7%.

Sampling error estimates

---> Sampling Errors Data of this survey may be affected by sampling errors due to use of a sample and not a complete enumeration. Therefore, certain differences can be expected in comparison with the real values obtained through censuses. Variances were calculated for the most important indicators: the variance table is attached with the final report. There is no problem in disseminating results at national or governorate level for the West Bank and Gaza Strip.

---> Non-Sampling Errors Non-statistical errors are probable in all stages of the project, during data collection or processing. This is referred to as non-response errors, response errors, interviewing errors, and data entry errors. To avoid errors and reduce their effects, great efforts were made to train the fieldworkers intensively. They were trained on how to carry out the interview, what to discuss and what to avoid, carrying out a pilot survey, as well as practical and theoretical training during the training course. Also data entry staff were trained on the data entry program that was examined before starting the data entry process. To stay in contact with progress of fieldwork activities and to limit obstacles, there was continuous contact with the fieldwork team through regular visits to the field and regular meetings with them during the different field visits. Problems faced by fieldworkers were discussed to clarify any issues. Non-sampling errors can occur at the various stages of survey implementation whether in data collection or in data processing. They are generally difficult to be evaluated statistically.

They cover a wide range of errors, including errors resulting from non-response, sampling frame coverage, coding and classification, data processing, and survey response (both respondent and interviewer-related). The use of effective training and supervision and the careful design of questions have direct bearing on limiting the magnitude of non-sampling errors, and hence enhancing the quality of the resulting data. The implementation of the survey encountered non-response where the case ( household was not present at home ) during the fieldwork visit

Graph and download economic data for Harmonized Index of Consumer Prices: All-Items HICP for Czech Republic (CP0000CZM086NEST) from Jan 1996 to Feb 2025 about Czech Republic, harmonized, all items, CPI, price index, indexes, and price.

Data is from the small-scale demonstration of the Intelligent Network Flow Optimization (INFLO) Prototype System and applications in Seattle, Washington. Connected vehicle systems were deployed in 21 vehicles in a scripted driving scenario circuiting this I-5 corridor northbound and southbound during morning rush hour. This data set contains speed harmonization messages that were recommended by the INFLO SPD-HARM algorithm and sent by the traffic management center to the connected vehicles, which provided drivers with the suggested speed while driving on the segment of I-5 that was included in the test. The objective of speed harmonization is to dynamically adjust and coordinate maximum appropriate vehicle speeds in response to downstream congestion, incidents, and weather or road conditions in order to maximize traffic throughput and reduce crashes.

Abstract copyright UK Data Service and data collection copyright owner.

The Cohort and Longitudinal Studies Enhancement Resources (CLOSER) project aims to maximise the use, value and impact of longitudinal research. It brings together leading longitudinal studies, the British Library and the UK Data Service, to stimulate interdisciplinary research, develop shared resources, provide training, and share expertise. Resources available from CLOSER include harmonised datasets (developed to facilitate cross-study comparisons) and CLOSER Discovery (a search engine that enables researchers to search and browse questionnaires and data from different longitudinal studies). The CLOSER website also includes a range of training materials focused on longitudinal studies and data. CLOSER is funded by the Economic and Social Research Council (ESRC) and is based at the UCL Institute of Education.

The CLOSER Work Package 1 provides data harmonisation of anthropometric measures between the National Survey of Health and Development (NSHD), National Child Development Study (NCDS), 1970 British Cohort Study (BCS70), Avon Longitudinal Study of Parents and Children (ALSPAC), and the Millennium Cohort Study (MCS).

The Harmonised Height, Weight and BMI in Five Longitudinal Cohort Studies: Millennium Cohort Study provides the harmonised data on height, weight and BMI for MCS.

Main Topics:
This study covers three harmonised anthropometric measures:

• height
• weight
• BMI