Abstract

In 2001, the World Bank in co-operation with the Republika Srpska Institute of Statistics (RSIS), the Federal Institute of Statistics (FOS) and the Agency for Statistics of BiH (BHAS), carried out a Living Standards Measurement Survey (LSMS). The Living Standard Measurement Survey LSMS, in addition to collecting the information necessary to obtain a comprehensive as possible measure of the basic dimensions of household living standards, has three basic objectives, as follows:

1. To provide the public sector, government, the business community, scientific institutions, international donor organizations and social organizations with information on different indicators of the population's living conditions, as well as on available resources for satisfying basic needs.

2. To provide information for the evaluation of the results of different forms of government policy and programs developed with the aim to improve the population's living standard. The survey will enable the analysis of the relations between and among different aspects of living standards (housing, consumption, education, health, labor) at a given time, as well as within a household.

3. To provide key contributions for development of government's Poverty Reduction Strategy Paper, based on analyzed data.

The Department for International Development, UK (DFID) contributed funding to the LSMS and provided funding for a further two years of data collection for a panel survey, known as the Household Survey Panel Series (HSPS). Birks Sinclair & Associates Ltd. were responsible for the management of the HSPS with technical advice and support provided by the Institute for Social and Economic Research (ISER), University of Essex, UK. The panel survey provides longitudinal data through re-interviewing approximately half the LSMS respondents for two years following the LSMS, in the autumn of 2002 and 2003. The LSMS constitutes Wave 1 of the panel survey so there are three years of panel data available for analysis. For the purposes of this documentation we are using the following convention to describe the different rounds of the panel survey: - Wave 1 LSMS conducted in 2001 forms the baseline survey for the panel - Wave 2 Second interview of 50% of LSMS respondents in Autumn/ Winter 2002 - Wave 3 Third interview with sub-sample respondents in Autumn/ Winter 2003

The panel data allows the analysis of key transitions and events over this period such as labour market or geographical mobility and observe the consequent outcomes for the well-being of individuals and households in the survey. The panel data provides information on income and labour market dynamics within FBiH and RS. A key policy area is developing strategies for the reduction of poverty within FBiH and RS. The panel will provide information on the extent to which continuous poverty is experienced by different types of households and individuals over the three year period. And most importantly, the co-variates associated with moves into and out of poverty and the relative risks of poverty for different people can be assessed. As such, the panel aims to provide data, which will inform the policy debates within FBiH and RS at a time of social reform and rapid change. KIND OF DATA

Geographic coverage

National coverage. Domains: Urban/rural/mixed; Federation; Republic

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The Wave 3 sample consisted of 2878 households who had been interviewed at Wave 2 and a further 73 households who were interviewed at Wave 1 but were non-contact at Wave 2 were issued. A total of 2951 households (1301 in the RS and 1650 in FBiH) were issued for Wave 3. As at Wave 2, the sample could not be replaced with any other households.

Panel design

Eligibility for inclusion

The household and household membership definitions are the same standard definitions as a Wave 2. While the sample membership status and eligibility for interview are as follows: i) All members of households interviewed at Wave 2 have been designated as original sample members (OSMs). OSMs include children within households even if they are too young for interview. ii) Any new members joining a household containing at least one OSM, are eligible for inclusion and are designated as new sample members (NSMs). iii) At each wave, all OSMs and NSMs are eligible for inclusion, apart from those who move outof-scope (see discussion below). iv) All household members aged 15 or over are eligible for interview, including OSMs and NSMs.

Following rules

The panel design means that sample members who move from their previous wave address must be traced and followed to their new address for interview. In some cases the whole household will move together but in others an individual member may move away from their previous wave household and form a new split-off household of their own. All sample members, OSMs and NSMs, are followed at each wave and an interview attempted. This method has the benefit of maintaining the maximum number of respondents within the panel and being relatively straightforward to implement in the field.

Definition of 'out-of-scope'

It is important to maintain movers within the sample to maintain sample sizes and reduce attrition and also for substantive research on patterns of geographical mobility and migration. The rules for determining when a respondent is 'out-of-scope' are as follows:

i. Movers out of the country altogether i.e. outside FBiH and RS. This category of mover is clear. Sample members moving to another country outside FBiH and RS will be out-of-scope for that year of the survey and not eligible for interview.

ii. Movers between entities Respondents moving between entities are followed for interview. The personal details of the respondent are passed between the statistical institutes and a new interviewer assigned in that entity.

iii. Movers into institutions Although institutional addresses were not included in the original LSMS sample, Wave 3 individuals who have subsequently moved into some institutions are followed. The definitions for which institutions are included are found in the Supervisor Instructions.

iv. Movers into the district of Brcko are followed for interview. When coding entity Brcko is treated as the entity from which the household who moved into Brcko originated.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data entry

As at Wave 2 CSPro was the chosen data entry software. The CSPro program consists of two main features to reduce to number of keying errors and to reduce the editing required following data entry: - Data entry screens that included all skip patterns. - Range checks for each question (allowing three exceptions for inappropriate, don't know and missing codes). The Wave 3 data entry program had more checks than at Wave 2 and DE staff were instructed to get all anomalies cleared by SIG fieldwork. The program was extensively tested prior to DE. Ten computer staff were employed in each Field Office and as all had worked on Wave 2 training was not undertaken.

Editing

Editing Instructions were compiled (Annex G) and sent to Supervisors. For Wave 3 Supervisors were asked to take more time to edit every questionnaire returned by their interviewers. The FBTSA examined the work twelve of the twenty-two Supervisors. All Supervisors made occasional errors with the Control Form so a further 100% check of Control Forms and Module 1 was undertaken by the FBTSA and SIG members.

Response rate

The panel survey has enjoyed high response rates throughout the three years of data collection with the wave 3 response rates being slightly higher than those achieved at wave 2. At wave 3, 1650 households in the FBiH and 1300 households in the RS were issued for interview. Since there may be new households created from split-off movers it is possible for the number of households to increase during fieldwork. A similar number of new households were formed in each entity; 62 in the FBiH and 63 in the RS. This means that 3073 households were identified during fieldwork. Of these, 3003 were eligible for interview, 70 households having either moved out of BiH, institutionalised or deceased (34 in the RS and 36 in the FBiH).

Interviews were achieved in 96% of eligible households, an extremely high response rate by international standards for a survey of this type.

In total, 8712 individuals (including children) were enumerated within the sample households (4796 in the FBiH and 3916 in the RS). Within in the 3003 eligible households, 7781 individuals aged 15 or over were eligible for interview with 7346 (94.4%) being successfully interviewed. Within cooperating households (where there was at least one interview) the interview rate was higher (98.8%).

A very important measure in longitudinal surveys is the annual individual re-interview rate. This is because a high attrition rate, where large numbers of respondents drop out of the survey over time, can call into question the quality of the data collected. In BiH the individual re-interview rates have been high for the survey. The individual re-interview rate is the proportion of people who gave an interview at time t-1 who also give an interview at t. Of those who gave a full interview at wave 2, 6653 also gave a full interview at wave 3. This represents a re-interview rate of 97.9% - which is extremely high by international standards. When we look at those respondents who have been interviewed at all three years of the survey there are 6409 cases which are available for longitudinal analysis, 2881 in the RS and 3528 in the FBiH. This represents 82.8% of the responding wave 1 sample, a

This table contains the data on identity, pathology, and dental wear used in the study. All dental wear measurements and the individuals they are associated with are included. (XLSX)

Raw Data on the compressive strength measurements of concrete samples that have been evaluated as follows: The goal is to investigate the influence of different mixing approaches on the compressive strength of four distinct concrete mixtures. Compressive strength is a critical property that directly impacts the structural performance of concrete. The study focuses on evaluating the compressive strength at three key curing periods (7 days, 28 days, and 56 days) for each of the four concrete mixtures. Three separate testing series are conducted to comprehensively assess the impact of various mixing techniques on concrete performance. The results reveal potential to reduce cement clinker content while sustaining performance through the selection of appropriate mixing processes Moist storage of the concrete cubes according to DIN EN 12390. Measurement procedure according to DIN EN 12390.

This software package performs joint quantum state and measurement tomography. The software is provided as Python source code. A description of the algorithms used is in "Joint Quantum State and Measurement Tomography with Incomplete Measurements" https://arxiv.org/abs/1803.08245Included are three example scripts that simulate data for one or two trapped ion systems with either symmetric or asymmetric measurements:- analysis_scripts/paper_simulations.py: produces all data and histograms shown in related publication with seed = 0. Also provides an example of symmetric measurements.- analysis_scripts/asym_simulations.py: produces simulated data from asymmetric measurements by similar methods as in previous script.- analysis_scripts/load_tutorial/load_simulations.py: gives an example of loading data with asymmetric measurements.

Data set and all details below are from the following Scientific Data publication: A measured energy use, solar production, and building air leakage dataset for a zero energy commercial building by Filip Agee, Leila Nikel, and Sydney Roberts

Abstract

This paper provides an open dataset of measured energy use, solar energy production, and building air leakage data from a 328 m2 (3,531 ft2) all-electric, zero energy commercial building in Virginia, USA. Over two years of energy use data were collected at 1-hour intervals using circuit-level energy monitors. Over six years of solar energy production data were measured at 1-hour resolution by 56 microinverters (presented as daily and monthly data in this dataset). The building air leakage data was measured post-construction per ASTM-E779 Standard Test Method for Determining Air Leakage Rate by Fan Pressurization and the United States Army Corps (USACE) Building Enclosure Testing procedure; both pressurization and depressurization results are provided. The architectural and engineering (AE) documents are provided to aid researchers and practitioners in reliable modeling of building performance. The paper describes the data collection methods, cleaning, and convergence with weather data. This dataset can be employed to predict, benchmark, and calibrate operational outcomes in zero energy commercial buildings.

Content

This dataset was developed from a single, non-random case study project. The building serves as a leasing office and community building for a national non-profit housing provider (referred hereafter as the “owner”). The owner’s mission is to create homes and communities that are healthy, sustainable, and affordable. The building was designed in 2013 and construction was completed in April 2014. The owner pursued EarthCraft Light Commercial (ECLC), a regional 3rd party green building program. The ECLC program was used to verify high performance design and construction targets were achieved. Table 1 provides an overview of the building specifications and the following section characterizes the data collection techniques for the energy use (demand over time – kWh), energy production, and building air leakage data.

Acknowledgements

The authors would like to acknowledge the assistance and support of the U.S. Department of Energy’s, Advanced Commercial Building Initiative (Contract Number: EE0006290, OSTI Identifier: 1351293), Community Housing Partners, Southface Energy Institute, Viridiant, and Arnold Design Studio.

Author information

Affiliations Myers-Lawson School of Construction, Virginia Tech, Blacksburg, VA, 24061, United States

Philip Agee & Leila Nikdel

Apogee Interactive, Tucker, GA, 30084, United States

Sydney Roberts

Contributions

Philip Agee installed the energy monitoring equipment, performed the building air leakage testing, led the manuscript and dataset development, coordination, obtained the informed consent from the owner, and performed the final review. Leila Nikdel organized, cleaned, and converged the dataset. She also wrote the dataset description and reviewed the final manuscript. Sydney Roberts oversaw the energy use and building air leakage data collection and reviewed the final manuscript. https://guides.lib.vt.edu/oasf/request.

Corresponding author

Correspondence to Philip Agee.

Overview The data included features wind, temperature, and turbulence measurements. Data Details Each met station (met.z18, met.z19, met.z21, and met.z23) consists of multiple levels of three-dimensional ultrasonic anemometers, RM Young 81000 (sampling frequency = 20 Hz), and temperature/relative humidity probes, Rotronics HC2S3 (sampling frequency = 1 Hz). The HC2S3 probes were housed in radiation shields to protect them from thermal radiation, and they were adequately ventilated. Moreover, an infrared gas analyzer (LI-7500 Open Path CO~2/H~2~O Analyzer) was collocated at 3-meter height at met.z18 (sampling frequency = 20 Hz) in a second phase of the experiment (June 2016). Raw data are collected by Campbell CR3000 dataloggers and successively parsed into 15-minutes data files. For each met station, multiple data files are outputted with each data file corresponding to a certain type of instrument and a specific measurement height. The types of instrument and measurement heights are specified by the name of the data file itself. For example, met station met.z19 consists of sonic anemometers measurements at 3-, 10-, and 17-meter height and temperature/relative humidity measurements at 3- and 17-meter height. Therefore, the following data files are outputted: 3m sonic anemometer sample file name: met.z19.00.20160502.171500.son03m.dat 10m sonic anemometer sample file name: met.z19.00.20160502.171500.son10m.dat 17m sonic anemometer sample file name: met.z19.00.20160502.171500.son17m.dat 3m T-RH sensor sample file name: met.z19.00.20160502.171500.trh03m.dat 17m T-RH sensor sample file name: met.z19.00.20160502.171500.trh17m.dat Note that in the "Primary Measurements/Variables" section, the variables sonic_u-wind, sonic_v-wind, sonic_w-wind represent the orthogonal u, v, and w wind velocities outputted by the sonic RM Young 81000, oriented with u-axis aligned east-west and v-axis aligned north-south. In this orientation, +u values = wind from the east, and +v values = wind from the north. Wind from below (updraft) = +w. Instruments' manuals and dataset samples are provided as attachments. Data Quality Raw data: no quality control (QC) is applied. Data are visually inspected at least weekly. Uncertainty RM Young 81000 Ultrasonic Anemometer Measurements Anything that blocks the acoustic signal path will degrade the measurement. If the path is blocked sufficiently, measurements cannot be made. The RM Young 81000 can make accurate measurements in driving rain, but light mist or heavy fog can allow droplets to accumulate on the transducer faces and block the measurement. Measurements may be made in driving snow, although frost and snow that adheres to the transducer face may block the measurement. Similarly, freezing rain on the transducer face may block the measurement. Rotronics HC2S3 Temperature and Relative Humidity Measurement This sensor requires minimal maintenance, but dust, debris, and salts on the filter cap may degrade sensor performance. Because of the remote location and difficulties in climbing the met towers, no maintenance of these sensors was performed during the field experiment. Licor LI-7500 Measurement The LI-7500 optical windows should be cleaned when necessary (by checking the diagnostic values). Rain, snow, fog, condensation, or dust deposition on the optical path of the instrument may affect the gas analyzer's performance and lead to less consistent/missing measurements. Because of the remote location and difficulties in climbing the met towers, no maintenance of these sensors was performed during the field experiment.

The 2020 Census Redistricting Data (P.L. 94-171) Noisy Measurement File (NMF) is an intermediate output of the 2020 Census Disclosure Avoidance System (DAS) TopDown Algorithm (TDA) (as described in Abowd, J. et al [2022] https://doi.org/10.1162/99608f92.529e3cb9, and implemented in the DAS 2020 Redistricting Production Code). The NMF was generated using the Census Bureau's implementation of the Discrete Gaussian Mechanism, calibrated to satisfy zero-Concentrated Differential Privacy with bounded neighbors.

The NMF values, called noisy measurements are the output of applying the Discrete Gaussian Mechanism to counts from the 2020 Census Edited File (CEF). They are generally inconsistent with one another (for example, in a county composed of two tracts, the noisy measurement for the county's total population may not equal the sum of the noisy measurements of the two tracts' total population), and frequently negative (especially when the population being measured was small), but are integer-valued. The NMF was later post-processed as part of the DAS code to take the form of microdata and to satisfy various constraints. The NMF documented here contains both the noisy measurements themselves as well as the data needed to represent the DAS constraints; thus, the NMF could be used to reproduce the steps taken by the DAS code to produce microdata from the noisy measurements by applying the production code base.

The 2020 Census Redistricting Data (P.L. 94-171) Noisy Measurement File includes zero-Concentrated Differentially Private (zCDP) (Bun, M. and Steinke, T [2016]) noisy measurements, implemented via the discrete Gaussian mechanism. These are estimated counts of individuals and housing units included in the 2020 Census Edited File (CEF), which includes confidential data initially collected in the 2020 Census of Population and Housing. The noisy measurements included in this file were subsequently post-processed by the TopDown Algorithm (TDA) to produce the 2020 Census Redistricting Data (P.L. 94-171) Summary File.

The NMF provides estimates of counts of persons in the CEF by various characteristics and combinations of characteristics including their reported race and ethnicity, whether they were of voting age, whether they resided in a housing unit or one of 7 group quarters types, and their census block of residence after the addition of discrete Gaussian noise (with the scale parameter determined by the privacy-loss budget allocation for that particular query under zCDP). Noisy measurements of the counts of occupied and vacant housing units by census block are also included. Lastly, data on constraints--information into which no noise was infused by the Disclosure Avoidance System (DAS) and used by the TDA to post-process the noisy measurements into the 2020 Census Redistricting Data (P.L. 94-171) Summary File --are provided.

AbstractObjectives: To assess feasibility, acceptability, and clinical sensibility of a novel survey, the Advance Care Planning (ACP) Engagement Survey in various health care settings. Setting: A target sample of 50 patients from each of primary care, hospital, cancer care, and dialysis care settings. Participants: A convenience sample of patients without cognitive impairment who could speak and read English was recruited. Patients 50 years and older were eligible in primary care; patients 80 and older or 55 years and older with clinical markers of advanced chronic disease were recruited in hospital; patients aged 19 and older were recruited in cancer and renal dialysis centres. Outcomes: We assessed feasibility, acceptability and clinical sensibility of the ACP Engagement Survey using a 6-point scale. The ACP Engagement Survey measures ACP processes (knowledge, contemplation, self-efficacy, readiness) on 5-point Likert scales and actions (yes/no). Results: 196 patients (38 to 96 years old, 50.5% women) participated. Mean (±standard deviation) time to administer was 48.8 ±19.6 minutes. Mean acceptability scores ranged from 3.2±1.3 in hospital to 4.7±0.9 in primary care and mean relevance ranged from 3.5±1.0 in hospital to 4.9±0.9 in dialysis centres (p values <0.001 for both). The mean process score was 3.1±0.6 and the mean action score was 11.2±5.6 (of a possible 25). Conclusions: The ACP Engagement Survey demonstrated feasibility and acceptability in out-patient settings, but was less feasible and acceptable among hospitalized patients due to length. A shorter version may improve feasibility. Engagement in ACP was low to moderate. Usage notesREADMEThe Readme file includes a list of files in this data package, and a description of the variables that were removed from the dataset to protect participant identity. Please see the "Data dictionary" for a description of the variables that were included in the dataset, and the "Summary table of indirect identifier data" for a summary of values reported at removed variables.Data Dictionary - Canadian ACP engagement sample BMJ OpenThis file describes the variables that were included in the dataset, and their allowable values.Canadian ACP engagement sample BMJ Open_data dictionary.xlsxCanadian ACP engagement survey pilotThis file contains the responses of 196 patients in acute care, primary care, cancer care and renal care to a 108-item ACP engagement survey. Process Measures (knowledge, contemplation, self-efficacy, and readiness, 5-point Likert scales) and Action Measures (yes/no whether an ACP behavior was completed) are included.Canadian ACP engagement sample_BMJ Open_indirect identifiers removed.xlsxSummary table of indirect identifier data - Canadian ACP engagement_BMJ OpenThis file contains descriptive analysis summary tables of indirect identifiers that were removed from the dataset.Canadian ACP engagement_BMJ Open_summary table of indirect identifier data.docx

The dataset include csv-file exports of the hysteresis, IRM and backfield measurements conducted with the model 8604 Lakeshore VSM at the University of Cologne. The header in each file show measurement details. Please note:

All hysteresis measurements are corrected for pole saturation by substracting the identically measured hysteresis of holmium oxide from the measurement data. Additionally, a fieldOffset correction was performed by the measurement program of Lake Shore, which may shift the hysteresis slightly along the X-axis. The weight of all samples is not correctly shown in the IRM-BF files but in the excel table "hysteresis parameters" in the folder "VSM measurement data analysed" of this data publication. The true volume of the samples remains unknown. If a (2) occurs behind the measurement, the first measurement was not successful and a second, identical one was performed using the same sample.

Abstract

The STEP (Skills Toward Employment and Productivity) Measurement program is the first ever initiative to generate internationally comparable data on skills available in developing countries. The program implements standardized surveys to gather information on the supply and distribution of skills and the demand for skills in labor market of low-income countries.

The uniquely-designed Household Survey includes modules that measure the cognitive skills (reading, writing and numeracy), socio-emotional skills (personality, behavior and preferences) and job-specific skills (subset of transversal skills with direct job relevance) of a representative sample of adults aged 15 to 64 living in urban areas, whether they work or not. The cognitive skills module also incorporates a direct assessment of reading literacy based on the Survey of Adults Skills instruments. Modules also gather information about family, health and language.

Geographic coverage

The survey covered the following regions: Western, Central, Greater Accra, Volta, Eastern, Ashanti, Brong Ahafo, Northern, Upper East and Upper West.
- Areas are classified as urban based on each country's official definition.

Analysis unit

The units of analysis are the individual respondents and households. A household roster is undertaken at the start of the survey and the individual respondent is randomly selected among all household members aged 15 to 64 included. The random selection process was designed by the STEP team and compliance with the procedure is carefully monitored during fieldwork.

Universe

The target population for the Ghana STEP survey comprises all non-institutionalized persons 15 to 64 years of age (inclusive) living in private dwellings in urban areas of the country at the time of data collection. This includes all residents except foreign diplomats and non-nationals working for international organizations. Exclusions : Military barracks were excluded from the Ghana target population.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Ghana sample design is a four-stage sample design. There was no explicit stratification but the sample was implicitly stratified by Region. [Note: Implicit stratification was achieved by sorting the PSUs (i.e., EACode) by RegnCode and selecting a systematic sample of PSUs.]

First Stage Sample The primary sample unit (PSU) was a Census Enumeration Area (EA). Each PSU was uniquely defined by the sample frame variables Regncode, and EAcode. The sample frame was sorted by RegnCode to implicitly stratify the sample frame PSUs by region. The sampling objective was to select 250 PSUs, comprised of 200 Initial PSUs and 50 Reserve PSUs. Although 250 PSUs were selected, only 201 PSUs were activated. The PSUs were selected using a systematic probability proportional to size (PPS) sampling method, where the measure of size was the population size (i.e., EAPopn) in a PSU.

Second Stage Sample The second stage sample unit is a PSU partition. It was considered necessary to partition 'large' PSUs into smaller areas to facilitate the listing process. After the partitioning of the PSUs, the survey firm randomly selected one partition. The selected partition was fully listed for subsequent enumeration in accordance with the field procedures.

Third Stage Sample The third stage sample unit (SSU) is a household. The sampling objective was to obtain interviews at 15 households within each selected PSU. The households were selected in each PSU using a systematic random method.

Fourth Stage Sample The fourth stage sample unit was an individual aged 15-64 (inclusive). The sampling objective was to select one individual with equal probability from each selected household.

Sample Size The Ghana firm's sampling objective was to obtain interviews from 3000 individuals in the urban areas of the country. In order to provide sufficient sample to allow for a worst case scenario of a 50% response rate the number of sampled cases was doubled in each selected PSU. Although 50 extra PSUs were selected for use in case it was impossible to conduct any interviews in one or more initially selected PSUs only one reserve PSU was activated. Therefore, the Ghana firm conducted the STEP data collection in a total of 201 PSUs.

Sampling methodologies are described for each country in two documents: (i) The National Survey Design Planning Report (NSDPR) (ii) The weighting documentation

Mode of data collection

Face-to-face [f2f]

Research instrument

The STEP survey instruments include: (i) a Background Questionnaire developed by the WB STEP team (ii) a Reading Literacy Assessment developed by Educational Testing Services (ETS).

All countries adapted and translated both instruments following the STEP Technical Standards: 2 independent translators adapted and translated the Background Questionnaire and Reading Literacy Assessment, while reconciliation was carried out by a third translator. The WB STEP team and ETS collaborated closely with the survey firms during the process and reviewed the adaptation and translation (using a back translation). In the case of Ghana, no translation was necessary, but the adaptation process ensured that the English used in the Background Questionnaire and Reading Literacy Assessment closely reflected local use.

• The survey instruments were both piloted as part of the survey pretest.
• The adapted Background Questionnaires are provided in English as external resources. The Reading Literacy Assessment is protected by copyright and will not be published.

Cleaning operations

STEP Data Management Process 1. Raw data is sent by the survey firm 2. The WB STEP team runs data checks on the Background Questionnaire data. - ETS runs data checks on the Reading Literacy Assessment data. - Comments and questions are sent back to the survey firm. 3. The survey firm reviews comments and questions. When a data entry error is identified, the survey firm corrects the data. 4. The WB STEP team and ETS check the data files are clean. This might require additional iterations with the survey firm. 5. Once the data has been checked and cleaned, the WB STEP team computes the weights. Weights are computed by the STEP team to ensure consistency across sampling methodologies. 6. ETS scales the Reading Literacy Assessment data. 7. The WB STEP team merges the Background Questionnaire data with the Reading Literacy Assessment data and computes derived variables.

Detailed information data processing in STEP surveys is provided in the 'Guidelines for STEP Data Entry Programs' document provided as an external resource. The template do-file used by the STEP team to check the raw background questionnaire data is provided as an external resource.

Response rate

An overall response rate of 83.2% was achieved in the Ghana STEP Survey. Table 20 of the weighting documentation provides the detailed percentage distribution by final status code.

Sampling error estimates

A weighting documentation was prepared for each participating country and provides some information on sampling errors. The weighting documentation is provided as an external resource.

Abstract

In 1992, Bosnia-Herzegovina, one of the six republics in former Yugoslavia, became an independent nation. A civil war started soon thereafter, lasting until 1995 and causing widespread destruction and losses of lives. Following the Dayton accord, BosniaHerzegovina (BiH) emerged as an independent state comprised of two entities, namely, the Federation of Bosnia-Herzegovina (FBiH) and the Republika Srpska (RS), and the district of Brcko. In addition to the destruction caused to the physical infrastructure, there was considerable social disruption and decline in living standards for a large section of the population. Along side these events, a period of economic transition to a market economy was occurring. The distributive impacts of this transition, both positive and negative, are unknown. In short, while it is clear that welfare levels have changed, there is very little information on poverty and social indicators on which to base policies and programs.

In the post-war process of rebuilding the economic and social base of the country, the government has faced the problems created by having little relevant data at the household level. The three statistical organizations in the country (State Agency for Statistics for BiH –BHAS, the RS Institute of Statistics-RSIS, and the FBiH Institute of Statistics-FIS) have been active in working to improve the data available to policy makers: both at the macro and the household level. One facet of their activities is to design and implement a series of household series. The first of these surveys is the Living Standards Measurement Study survey (LSMS). Later surveys will include the Household Budget Survey (an Income and Expenditure Survey) and a Labor Force Survey. A subset of the LSMS households will be re-interviewed in the two years following the LSMS to create a panel data set.

The three statistical organizations began work on the design of the Living Standards Measurement Study Survey (LSMS) in 1999. The purpose of the survey was to collect data needed for assessing the living standards of the population and for providing the key indicators needed for social and economic policy formulation. The survey was to provide data at the country and the entity level and to allow valid comparisons between entities to be made.

The LSMS survey was carried out in the Fall of 2001 by the three statistical organizations with financial and technical support from the Department for International Development of the British Government (DfID), United Nations Development Program (UNDP), the Japanese Government, and the World Bank (WB). The creation of a Master Sample for the survey was supported by the Swedish Government through SIDA, the European Commission, the Department for International Development of the British Government and the World Bank.

The overall management of the project was carried out by the Steering Board, comprised of the Directors of the RS and FBiH Statistical Institutes, the Management Board of the State Agency for Statistics and representatives from DfID, UNDP and the WB. The day-to-day project activities were carried out by the Survey Mangement Team, made up of two professionals from each of the three statistical organizations.

The Living Standard Measurement Survey LSMS, in addition to collecting the information necessary to obtain a comprehensive as possible measure of the basic dimensions of household living standards, has three basic objectives, as follows:

1. To provide the public sector, government, the business community, scientific institutions, international donor organizations and social organizations with information on different indicators of the population’s living conditions, as well as on available resources for satisfying basic needs.

2. To provide information for the evaluation of the results of different forms of government policy and programs developed with the aim to improve the population’s living standard. The survey will enable the analysis of the relations between and among different aspects of living standards (housing, consumption, education, health, labor) at a given time, as well as within a household.

3. To provide key contributions for development of government’s Poverty Reduction Strategy Paper, based on analyzed data.

Geographic coverage

National coverage. Domains: Urban/rural/mixed; Federation; Republic

Kind of data

Sample survey data [ssd]

Sampling procedure

A total sample of 5,400 households was determined to be adequate for the needs of the survey: with 2,400 in the Republika Srpska and 3,000 in the Federation of BiH. The difficulty was in selecting a probability sample that would be representative of the country's population. The sample design for any survey depends upon the availability of information on the universe of households and individuals in the country. Usually this comes from a census or administrative records. In the case of BiH the most recent census was done in 1991. The data from this census were rendered obsolete due to both the simple passage of time but, more importantly, due to the massive population displacements that occurred during the war.

At the initial stages of this project it was decided that a master sample should be constructed. Experts from Statistics Sweden developed the plan for the master sample and provided the procedures for its construction. From this master sample, the households for the LSMS were selected.

Master Sample [This section is based on Peter Lynn's note "LSMS Sample Design and Weighting - Summary". April, 2002. Essex University, commissioned by DfID.]

The master sample is based on a selection of municipalities and a full enumeration of the selected municipalities. Optimally, one would prefer smaller units (geographic or administrative) than municipalities. However, while it was considered that the population estimates of municipalities were reasonably accurate, this was not the case for smaller geographic or administrative areas. To avoid the error involved in sampling smaller areas with very uncertain population estimates, municipalities were used as the base unit for the master sample.

The Statistics Sweden team proposed two options based on this same method, with the only difference being in the number of municipalities included and enumerated. For reasons of funding, the smaller option proposed by the team was used, or Option B.

Stratification of Municipalities

The first step in creating the Master Sample was to group the 146 municipalities in the country into three strata- Urban, Rural and Mixed - within each of the two entities. Urban municipalities are those where 65 percent or more of the households are considered to be urban, and rural municipalities are those where the proportion of urban households is below 35 percent. The remaining municipalities were classified as Mixed (Urban and Rural) Municipalities. Brcko was excluded from the sampling frame.

Urban, Rural and Mixed Municipalities: It is worth noting that the urban-rural definitions used in BiH are unusual with such large administrative units as municipalities classified as if they were completely homogeneous. Their classification into urban, rural, mixed comes from the 1991 Census which used the predominant type of income of households in the municipality to define the municipality. This definition is imperfect in two ways. First, the distribution of income sources may have changed dramatically from the pre-war times: populations have shifted, large industries have closed and much agricultural land remains unusable due to the presence of land mines. Second, the definition is not comparable to other countries' where villages, towns and cities are classified by population size into rural or urban or by types of services and infrastructure available. Clearly, the types of communities within a municipality vary substantially in terms of both population and infrastructure.

However, these imperfections are not detrimental to the sample design (the urban/rural definition may not be very useful for analysis purposes, but that is a separate issue). [Note: It may be noted that the percent of LSMS households in each stratum reporting using agricultural land or having livestock is highest in the "rural" municipalities and lowest in the "urban" municipalities. However, the concentration of agricultural households is higher in RS, so the municipality types are not comparable across entities. The percent reporting no land or livestock in RS was 74.7% in "urban" municipalities, 43.4% in "mixed" municipalities and 31.2% in "rural" municipalities. Respective figures for FbiH were 88.7%, 60.4% and 40.0%.]

The classification is used simply for stratification. The stratification is likely to have some small impact on the variance of survey estimates, but it does not introduce any bias.

Selection of Municipalities

Option B of the Master Sample involved sampling municipalities independently from each of the six strata described in the previous section. Municipalities were selected with probability proportional to estimated population size (PPES) within each stratum, so as to select approximately 50% of the mostly urban municipalities, 20% of the mixed and 10% of the mostly rural ones. Overall, 25 municipalities were selected (out of 146) with 14 in the FbiH and 11 in the RS. The distribution of selected municipalities over the sampling strata is shown below.

Stratum / Total municipalities Mi / Sampled municipalities mi 1. Federation, mostly urban / 10 / 5 2. Federation, mostly mixed / 26 / 4 3. Federation, mostly rural / 48 / 5 4. RS, mostly urban /4 / 2 5. RS, mostly mixed /29 / 5 6. RS, mostly rural / 29 / 4

Note: Mi is the total number of municipalities in stratum i (i=1, … , 6); mi is the number of municipalities selected from stratum

Companion dataset of the manuscript: Fu Li, Umberto Villa, Seonyeong Park, Mark A. Anastasio. Three-dimensional stochastic numerical breast phantoms for enabling virtual imaging trials of ultrasound computed tomography. Arxiv preprint 2106.02744 (2021) This dataset includes a collection of 52 two-dimensional slices of numerical breast phantoms (NBPs) and corresponding ultrasound computed tomography (USCT) simulated measurement data. The anatomical structures of these NBPs were obtained by use of tools from the Virtual Imaging Clinical Trial for Regulatory Evaluation (VICTRE) project. More details on the modification and extension of the VICTRE NBPs for use in USCT studies can be found in the accompanying paper. The NBPs included in this dataset are representative of four ACR BI-RADS breast composition types: A. The breast is almost entirely fatty B. There are scattered areas of fibroglandular density C. The breasts is heterogeneously dense D. The breast is extremely dense Each NBP contains 2D maps of tissue labels, speed of sound, acoustic attenuation, density. A low-resolution speed-of-sound map is also provided to reproduce the FWI reconstruction results presented in the accompanying paper. Corresponding USCT measurement data were simulated by modeling 2D wave propagation in lossy heterogeneous media using a time explicit pseudospectral wave propagation solver. The dataset consists of three folders: The 2d_slices folder contains the 52 slices extracted from 3D NBPs. The measurements folder contains simulated measurement data corresponding to each slice. The imaging_system folder contains information about the 2D imaging system (excitation source, transducer coordinates) In addition, the following helper Matlab scripts are included: read_data.m: Helper function to load and visualize the excitation source and transducer locations. read_waveform_data.m: Helper function to read the .h5 files containing the measurement data. Each slice is saved as a binary MATLAB file (.mat) and contains the following variables label: tissue label map with [2560,2560] pixels and 0.1mm pixel size. Tissue types are denoted using the following labels: water (0), fat (1), skin (2), glandular (29), ligament (88), lesion (200). sos: speed of sound map (mm/μs) with [2560,2560] pixels and 0.1 mm pixel size. Data is stored as data type float32. aa: acoustic attenuation map (Np/m/MHzy) with [2560,2560] pixels and 0.1mm pixel size. Data is stored as data type float32. density: density map (kg/mm3) with [2560,2560] pixels and 0.1 mm pixel size. Data is stored as data type float32. sos_ini: low resolution speed of sound map (mm/μs) with [1280,1280] pixels and 0.2mm pixel size. Data is stored as data type float32. This is the initial guess used in the speed of sound reconstructions in our paper. y: attenuation exponent used for simulation. seed: phantom id type: breast composition type (A-D) The simulated measurement data is saved in hdf5 format. Measurement data corresponding the i-th emitting transducer is stored with hdf5 key equal to the transducer index as a two-dimensional array of size [1024,4250]. Here, the rows represent the receiver index, and the columns the time sample. The sampling frequency is 25MHZ. Because of file size limitations, measurement data for each slice has been divided into 8 chunks, containing data from 128 receivers each. The imaging_system folder contains information regarding the 2D imaging system. source300.mat describes the time profile of the exitation pulse. It consists of 300 time samples at a sampling frequency of 25Mhz. locations1024.mat provide the xy coordinates (mm)of the location of each transducer Data type is float32. Array size is [2x1024]. Warning: This is a very large dataset (~1TB). Please check out our download script written in python.

Update: New version includes additional samples taken in November 2022.

Dataset Description

This dataset is a large-scale set of measurements for RSS-based localization. The data consists of received signal strength (RSS) measurements taken using the POWDER Testbed at the University of Utah. Samples include either 0, 1, or 2 active transmitters.

The dataset consists of 5,214 unique samples, with transmitters in 5,514 unique locations. The majority of the samples contain only 1 transmitter, but there are small sets of samples with 0 or 2 active transmitters, as shown below. Each sample has RSS values from between 10 and 25 receivers. The majority of the receivers are stationary endpoints fixed on the side of buildings, on rooftop towers, or on free-standing poles. A small set of receivers are located on shuttles which travel specific routes throughout campus.

Dataset Description Sample Count Receiver Count

No-Tx Samples 46 10 to 25

1-Tx Samples 4822 10 to 25

2-Tx Samples 346 11 to 12

The transmitters for this dataset are handheld walkie-talkies (Baofeng BF-F8HP) transmitting in the FRS/GMRS band at 462.7 MHz. These devices have a rated transmission power of 1 W. The raw IQ samples were processed through a 6 kHz bandpass filter to remove neighboring transmissions, and the RSS value was calculated as follows:

(RSS = \frac{10}{N} \log_{10}\left(\sum_i^N x_i^2 \right) )

Measurement Parameters Description

Frequency 462.7 MHz

Radio Gain 35 dB

Receiver Sample Rate 2 MHz

Sample Length N=10,000

Band-pass Filter 6 kHz

Transmitters 0 to 2

Transmission Power 1 W

Receivers consist of Ettus USRP X310 and B210 radios, and a mix of wide- and narrow-band antennas, as shown in the table below Each receiver took measurements with a receiver gain of 35 dB. However, devices have different maxmimum gain settings, and no calibration data was available, so all RSS values in the dataset are uncalibrated, and are only relative to the device.

Usage Instructions

Data is provided in .json format, both as one file and as split files.

import json data_file = 'powder_462.7_rss_data.json' with open(data_file) as f: data = json.load(f)

The json data is a dictionary with the sample timestamp as a key. Within each sample are the following keys:

rx_data: A list of data from each receiver. Each entry contains RSS value, latitude, longitude, and device name.

tx_coords: A list of coordinates for each transmitter. Each entry contains latitude and longitude.

metadata: A list of dictionaries containing metadata for each transmitter, in the same order as the rows in tx_coords

File Separations and Train/Test Splits

In the separated_data.zip folder there are several train/test separations of the data.

all_data contains all the data in the main JSON file, separated by the number of transmitters.

stationary consists of 3 cases where a stationary receiver remained in one location for several minutes. This may be useful for evaluating localization using mobile shuttles, or measuring the variation in the channel characteristics for stationary receivers.

train_test_splits contains unique data splits used for training and evaluating ML models. These splits only used data from the single-tx case. In other words, the union of each splits, along with unused.json, is equivalent to the file all_data/single_tx.json.

The random split is a random 80/20 split of the data.

special_test_cases contains the stationary transmitter data, indoor transmitter data (with high noise in GPS location), and transmitters off campus.

The grid split divides the campus region in to a 10 by 10 grid. Each grid square is assigned to the training or test set, with 80 squares in the training set and the remainder in the test set. If a square is assigned to the test set, none of its four neighbors are included in the test set. Transmitters occuring in each grid square are assigned to train or test. One such random assignment of grid squares makes up the grid split.

The seasonal split contains data separated by the month of collection, in April, July, or November

The transportation split contains data separated by the method of movement for the transmitter: walking, cycling, or driving. The non-driving.json file contains the union of the walking and cycling data.

campus.json contains the on-campus data, so is equivalent to the union of each split, not including unused.json.

Digital Surface Model

The dataset includes a digital surface model (DSM) from a State of Utah 2013-2014 LiDAR survey. This map includes the University of Utah campus and surrounding area. The DSM includes buildings and trees, unlike some digital elevation models.

To read the data in python:

import rasterio as rio import numpy as np import utm

dsm_object = rio.open('dsm.tif') dsm_map = dsm_object.read(1) # a np.array containing elevation values dsm_resolution = dsm_object.res # a tuple containing x,y resolution (0.5 meters) dsm_transform = dsm_object.transform # an Affine transform for conversion to UTM-12 coordinates utm_transform = np.array(dsm_transform).reshape((3,3))[:2] utm_top_left = utm_transform @ np.array([0,0,1]) utm_bottom_right = utm_transform @ np.array([dsm_object.shape[0], dsm_object.shape[1], 1]) latlon_top_left = utm.to_latlon(utm_top_left[0], utm_top_left[1], 12, 'T') latlon_bottom_right = utm.to_latlon(utm_bottom_right[0], utm_bottom_right[1], 12, 'T')

Dataset Acknowledgement: This DSM file is acquired by the State of Utah and its partners, and is in the public domain and can be freely distributed with proper credit to the State of Utah and its partners. The State of Utah and its partners makes no warranty, expressed or implied, regarding its suitability for a particular use and shall not be liable under any circumstances for any direct, indirect, special, incidental, or consequential damages with respect to users of this product.

DSM DOI: https://doi.org/10.5069/G9TH8JNQ

1. Ordination and clustering methods are widely applied to ecological data that are nonnegative, for example species abundances or biomasses. These methods rely on a measure of multivariate proximity that quantifies differences between the sampling units (e.g. individuals, stations, time points), leading to results such as: (i) ordinations of the units, where interpoint distances optimally display the measured differences; (ii) clustering the units into homogeneous clusters; or (iii) assessing differences between pre-specified groups of units (e.g., regions, periods, treatment-control groups). 2. These methods all conceal a fundamental question: To what extent are the differences between the sampling units, computed according to the chosen proximity function, capturing the "size" in the multivariate observations, or their "shape"? "Size" means the overall level of the measurements: for example, some samples contain higher total abundances or more biomass, others less. "Shape" means the relative levels of the measurements: for example, some samples have different relative abundances, i.e. different compositions. To answer this question, several well-known proximity measures are considered and applied to two data sets, one of which is used in a simulation exercise where "shape" differences have been eliminated by randomization. For any data set and any proximity measure, a quantification is achieved of the proportion of "size" variance and "shape" variance that the measure is capturing, as well as the proportion of variance that confounds "size" and "shape" together. 3. The results consistently show that the Bray-Curtis coefficient incorporates both "size" and "shape" differences, to varying degrees. These two components are thus always confounded by this proximity measure in the determination of ordinations, clusters, group comparisons and relations to environmental variables. 4. There are several implications of these results, the main one being that researchers should be aware of this issue when they choose a proximity measure. They should compute the "size" and "shape" components for their particular data sets, since this can radically affect the interpretation of their results. It is recommended to separate these components: analysing total abundances or other measures of "size" by univariate methods, and using multivariate analysis on the relative abundances where size has been specifically excluded.

CERN-LHC. The ratio of the production cross sections times branching fractions $\sigma(B_c^{\pm})B(B_c^{\pm} \to J/\psi \pi^{\pm}) /((\sigma(B^{\pm}) B(B^{\pm} \to J/\psi K^{\pm}))$ is studied in proton-proton collisions at a center-of-mass energy of 7 TeV with the CMS detector at the LHC. The kinematic region investigated requires $B_c^{\pm}$ and $B^{\pm}$ mesons with transverse momentum $p_T$ > 15 GeV and rapidity |y| < 1.6. The data sample corresponds to an integrated luminosity of 5.1 fb$^{-1}$. The ratio is determined to be [0.48 $\pm$ 0.05(stat) $\pm$ 0.03(syst) $\pm$ 0.05 ($\tau_{Bc})$]% . The $B_c^{\pm} \to J/\psi \pi^{\pm} \pi^{\pm} \pi^{\mp}$ is also observed in the same data sample. Using a model-independent method developed to measure the efficiency given the presence of resonant behaviour in the three-pion system, the ratio of the branching fractions $B(B_c^{\pm} \to J/\psi \pi^{\pm} \pi^{\pm} \pi^{\mp})/B(B_c^{\pm} \to J/\psi \pi^{\pm})$ is measured to be 2.55 $\pm$ 0.80(stat) $\pm$ 0.33(syst) $\mathrm{^{+ 0.04}_{-0.01} }(\tau_{Bc}$), consistent with the previous LHCb result.

Full 6.5-day CW measurement for Drift Detection

Experiment:

-The dataset was captured using the Chipwhisperer CW308, with the target device being STM32F3 with an ARM Cortex-M4

-The target device performed an AES-128 encryption while we measured the leakage traces

-The experiment lasted for approximately 6.5 days

-The data is organized in parts of 100k traces each. Each 100k-sized part was captured in approx. 38 minutes. Each trace has 5k time samples (features). The original experiment has a total of 254 parts of 100k traces each.

-Every 100k-trace data part is called tracesi.mat and comes together with labeli.mat, for indexes i = 1, 2, ..., 254

-The labeli.mat is the value of a single sboxoutput of AES-128 i.e. the label ranges in the set {0,1,...,255}. We assume that successfully recovering the sboxoutput implies successfully recovering the respective key byte of AES-128.

We also have a reduced version of the dataset available here:

https://doi.org/10.21942/uva.25061858

UWB Positioning and Tracking Data Set UWB positioning data set contains measurements from four different indoor environments. The data set contains measurements that can be used for range-based positioning evaluation in different indoor environments. # Measurement system The measurements were made using 9 DW1000 UWB transceivers (DWM1000 modules) connected to the networked RaspberryPi computer using in-house radio board SNPN_UWB. 8 nodes were used as positioning anchor nodes with fixed locations in individual indoor environment and one node was used as a mobile positioning tag. Each UWB node is designed arround the RaspberryPi computer and are wirelessly connected to the measurement controller (e.g. laptop) using Wi-Fi and MQTT communication technologies. All tag positions were generated beforehand to as closelly resemble the human walking path as possible. All walking path points are equally spaced to represent the equidistand samples of a walking path in a time-domain. The sampled walking path (measurement TAG positions) are included in a downloadable data set file under downloads section. # Folder structure Folder structure is represented below this text. Folder contains four subfolders named by the indoor environments measured during the measurement campaign and a folder raw_data where raw measurement data is saved. Each environment folder has a anchors.csv file with anchor names and locations, .json file data.json with measurements, file walking_path.csv file with tag positions and subfolder floorplan with floorplan.dxf (AutoCAD format), floorplan.png and floorplan_track.jpg. Subfolder raw_data contains raw data in subfolders named by the four indor environments where the measurements were taken. Each location subfolder contains a subfolder data where data from each tag position from the walking_path.csv is collected in a separate folder. There is exactly the same number of folders in data folder as is the number of measurement points in the walking_path.csv. Each measurement subfolder contains 48 .csv files named by communication channel and anchor used for those measurements. For example: ch1_A1.csv contains all measurements at selected tag location with anchor A1 on UWB channel ch1. The location folder contains also anchors.csv and walking_path.csv files which are identical to the files mentioned previously. The last folder in the data set is the technical_validation folder, where results of technical validation of the data set are collected. They are separated into 8 subfolders: - cir_min_max_mean - los_nlos - positioning_wls - range - range_error - range_error_A6 - range_error_histograms - rss The organization of the data set is the following: data_set + location0 - anchors.csv - data.json - walking_path.csv + floorplan - floorplan.dxf - floorplan.png - floorplan_track.jpg - walking_path.csv + location1 - ... + location2 - ... + location3 - ... + raw_data + location0 + data + 1.07_9.37_1.2 - ch1_A1.csv - ch7_A8.csv - ... + 1.37_9.34_1.2 - ... + ... + location1 + ... + location2 + ... + location3 + ... + technical validation + cir_min_max_mean + positioning_wls + range + range_error + range_error_histograms + rss - LICENSE - README # Data format Raw measurements are saved in .csv files. Each file starts with a header, where first line represents the version of the file and the second line represents the data column names. The column names have a missing column name. Actual column names included in the .csv files are: TAG_ID ANCHOR_ID X_TAG Y_TAG Z_TAG X_ANCHOR Y_ANCHOR Z_ANCHOR NLOS RANGE FP_INDEX RSS RSS_FP FP_POINT1 FP_POINT2 FP_POINT3 STDEV_NOISE CIR_POWER MAX_NOISE RXPACC CHANNEL_NUMBER FRAME_LENGTH PREAMBLE_LENGTH BITRATE PRFR PREAMBLE_CODE CIR (starts with this column; all columns until the end of the line represent the channel impulse response) # Availability of CODE Code for data analysis and preprocessing of all data available in this data set is published on GitHub: https://github.com/KlemenBr/uwb_positioning.git The code is licensed under the Apache License 2.0. # Authors and License Author of data set in this repository is Klemen Bregar, klemen.bregar@ijs.si. This work is licensed under a Creative Commons Attribution 4.0 International License. # Funding The research leading to the data collection has been partially funded from the European Horizon 2020 Programme project eWINE under grant agreement No. 688116, the Slovenian Research Agency under Grant numbers P2-0016, J2-2507 and bilateral project with Grant number BI-ME/21-22-007.

Note that the Bernoulli-valued measurements for sex give the observed response process certainty that the sample unit is female.

The SLF avalanche warning service operates an extensive network of manual measuring sites. The sites are distributed throughout the Swiss Alps and predominantly situated in intermediate altitude zones, between 1000 and 2000 m. Some of the measurement series already span very long periods and are therefore highly valued; the data are also used for climatological and hydrological purposes. The measuring sites are in fixed locations, which are flat and wind-protected. The observers who perform the measurements are trained and paid by the SLF. Data is collected, as far as possible, from the beginning of November until the end of April and after that until half of the measuring site is snow-free. On some measuring sites event-based measurements are also collected during the summer months. If possible, measurements take place between 7 and 7.30 am local time.

The following variables are measured at all measuring sites:

• snow depth and 24-hour new snow

at numerous sites this additional variable is measured:

• water equivalent of 24-hour new snow (height of the water column in millimeters, if the new snow sample is melted, without changing the base area)

When using the data, please consider and adhere to the associated Terms of Use To download live data use our API. To download data older than 7 days use our File Download.

In this article, we investigate the Gaussian graphical model inference problem in a novel setting that we call erose measurements, referring to irregularly measured or observed data. For graphs, this results in different node pairs having vastly different sample sizes which frequently arises in data integration, genomics, neuroscience, and sensor networks. Existing works characterize the graph selection performance using the minimum pairwise sample size, which provides little insights for erosely measured data, and no existing inference method is applicable. We aim to fill in this gap by proposing the first inference method that characterizes the different uncertainty levels over the graph caused by the erose measurements, named GI-JOE (Graph Inference when Joint Observations are Erose). Specifically, we develop an edge-wise inference method and an affiliated FDR control procedure, where the variance of each edge depends on the sample sizes associated with corresponding neighbors. We prove statistical validity under erose measurements, thanks to careful localized edge-wise analysis and disentangling the dependencies across the graph. Finally, through simulation studies and a real neuroscience data example, we demonstrate the advantages of our inference methods for graph selection from erosely measured data. Supplementary materials for this article are available online.